AU2010270227B2 - Method for predicting efficacy of drugs in a patient - Google Patents

Abstract

The present invention concerns a method for predicting the relative efficacy of a plurality of drugs for treating a tumour in an individual comprising the molecular characterization of the tumour, and the calculation of a score for the plurality of drugs essentially based on the percentage of deregulated target gene.

Description

WO 2011/003911 PCT/EP2010/059648 METHOD FOR PREDICTING EFFICACY OF DRUGS IN A PATIENT FIELD OF INVENTION The present invention relates to the field of medicine, in particular personalized 5 medicine in cancer therapy. BACKGROUND OF THE INVENTION The therapeutic care of the patients having cancer is primarily based on surgery, radiotherapy and chemotherapy which have to be used according to standard protocols. The 10 curative surgery consists in removal of all the tumoral mass. However, this is not always possible to guarantee the absence of any residual disease after the ablation of the observable part of the tumour, even by experienced surgeons. This is why the surgery is generally used in combination is with radiotherapy and/or chemotherapy. Chemotherapy and/or radiotherapy can be used as neoadjuvant therapy or auxiliary or adjuvant therapy, or alone when the 15 surgery is impossible. Neoadjuvant therapy is usually used when the tumoral mass is too important and requires a reduction before surgery. Auxiliary or adjuvant chemotherapy is used to treat the residual tumoral diseases and to limit the local recurrences or the metastatic relapses. When a tumour is detected at an inoperable stage, then the therapeutic care is only based on chemotherapy and/or radiotherapy. Surgery is marginally used in this context and 20 has palliative objectives. In any case, the choice of chemotherapy always raises the following questions: Which drug or combination of drugs is adapted to this type of cancer? What is the most adapted therapeutic strategy for this patient? What are the chances for observing a therapeutic benefit with the selected drugs? 25 The current medical practice consists in treating the patients according to the existing therapeutic protocols. In the majority of the cases, the choice of the therapeutic protocol is based on the anatomo-pathological and clinical data. These protocols apply in first, second, even third therapeutic line. When there is therapeutic failure, or for the metastatic stages, certain patient are included in clinical trials generally using broad selection criteria defining 30 primarily, the location of the primitive tumour, the extension of the disease, the situation of the vital functions of the patient and certain specific contraindications of the drug under trial.

WO 2011/003911 PCT/EP2010/059648 2 Whatever the therapeutic approach (standard or clinical trial), only part of the treated population profits from the treatment whereas the remainder of the patients do not respond and show a progressing disease even under treatment. To improve this situation, since many years, physicians and researchers are trying to 5 identify markers for predicting the efficacy of the treatments for a given patient and to be able to adapt the treatment of each patient. Thus the concept of personalized medicine consists in adapting the therapeutic decision according to the anatomo-pathological, clinical characteristics but especially of the biological characteristics of the tumour. Several examples are known without representing a solution useful for any patient 10 having a cancer. A first approach was the so-called "test-companion" assay, used for the first time for the trastuzumab (Herceptin@ ), a monoclonal antibody targeting the Her2/Ncu receptor. In breast cancers, this drug is administered only when an amplification/overexpression of this receptor is observed. However, this overexpression does not guarantee a therapeutic response. 15 Some resistances to Herceptin@ can be explained by an activation of the Akt pathway, for instance. The association of an mTOR inhibitor (targeting the Akt pathway) can restore the sensitivity to Herceptin. Nevertheless, for some patients, the therapeutic benefit was observed in the absence of an amplification of the receptor. The measurement of the expression level of the Her2 receptor is the first example of 20 test companion and the majority of pharmaceutical companies or researchers are trying to reproduce this model considered as the first example of personalized medicine. The following examples are relevant to illustrate the concept of selection of the patients who could profit from a given drug: - Mutation or amplification of EGFR receptor and erlotinib / gefitinib; 25 - Mutations c-Kit/PDGFRa and imatinib; - Translocation of Ber-Abl and imatinib; - Amplification of HER2 and HER2 inhibitors; - Amplification of TOP2A and anthracyclines; - Deletion of PTEN and mTOR inhibitors; 30 - Amplification of FGFR1 and FGFR1 inhibitors; - ERCC 1 negative-treatment and platinium salts; - RAS mutations and treatment of colon cancer - etc......

WO 2011/003911 PCT/EP2010/059648 3 In the case of breast cancer, prognostic molecular signatures, such as the tests Mamaprint@ (developed by the Agendia company) or OncotypeDX@ (company Genomic Health) are available. These signatures are used to determine if an auxiliary chemotherapy is necessary or not. But, although these tests make it possible to conclude on the need from an 5 auxiliary chemotherapy, they do not make it possible to select the optimal therapy. In short, the concept of personalized medicine corresponds to a selection of patients on biological criteria to increase the chances of response to a given therapy. Currently, these tests companion are rather used for the treatments by targeted therapies and make it possible to select the patients likely to profit from a given therapy but not to select the best therapy for a 10 given patient. This is a major conceptual difference which constitutes the main interest of the present invention compared to the other markers proposed to date. The anomalies of strong amplitude of the gene copy number (amplifications or deletions) modify the levels of gene expression. This mechanism of genomic deregulation is involved in the ontogenesis of many cancers. Amplifications of the EGFR gene are found in 15 approximately 30% of lung cancers. The inhibition of EGFR in case of amplification is associated with a significant benefit in this same pathology. Similarly, MYCN is amplified in approximately 25% of the neuroblastoma and several studies showed the prognostic value of this anomaly in this pathology. Other oncogenes / anti-oncogenes tumourr suppressor genes) are frequently amplified/deleted in other types of tumours such HER, PTEN, PUTS, and the 20 like. Breast cancer presents an important frequency of chromosomal aberrations. Gene HER2 (ErbB2) is amplified in 10 to 20% of the cases. This amplification is associated with a hyper-expression of the Her2 protein and is involved in the tumoral transformation. A therapeutic strategy based on the targeting of this anomaly showed a benefit in the patients 25 having HER2-positive breast cancer. In addition, the gene coding for the topoisomerase II is amplified in approximately 7% of breast cancers. This amplification is correlated with a good sensitivity to the anthracyclines, a class of drugs targeting the topoisomerase II. Other anomalies have often been observed in breast cancer. AIBI gene is amplified in 10% of the cases, and leads to ontogenesis via the activation of AKT by the IGFR. FGF1R gene is 30 amplified in 10% of case. The targeting of this protein by a tyrosine kinase inhibitor leads in vitro to a reduction of the cell multiplication. Similarly, amplifications of the genes EGFR, IGF1R or the deletions of PTEN can be treated by molecules targeting EGFR, IGF1R or mTOR, respectively.

WO 2011/003911 PCT/EP2010/059648 4 In the scientific literature, certain works, among which those of A. Potti et al, propose a prediction of the drugs efficacy, primarily cytotoxicity, based on the analysis of the expression of genes selected from experiments on well-established cell lines (panel NCI60). These data allow the identification of expression profiles associated with the response for 5 each tested molecule and this prediction is transposed to the human tumours. However, if this approach allows a molecule by molecule prediction, it does not allow the comparison of the efficacy of each molecule for a given patient in order to select the best drug. In addition, the one skilled in the art knows the limitations of in vitro model to perform in vivo predictions. These approaches tend to enrich the patient cohort for a given chemotherapy rather than to 10 select a targeted individual therapy for a given patient on the basis of the intrinsic tumoral characteristics. However, the choice of the appropriate chemotherapy in cancer treatment is a crucial issue. Indeed, most of the chemotherapies have very significant adverse effects and an erroneous choice (i.e., treatment without any therapeutic benefit) could lead to a cancer 15 progression. Up today, there is no marker efficient to select the most optimal therapeutic strategy for a given individual having a cancer. Accordingly, there is a strong need to methods of personalized medicine in the field of cancer treatment allowing the selection for a given individual of the most appropriate chemotherapy strategy. 20 SUMMARY OF THE INVENTION The present invention concerns a method for predicting the relative efficacy of a plurality of drugs for treating a cancer in a patient comprising: - characterizing molecular anomalies of a tumour or metastase sample from the patient in comparison to a normal sample from the same patient, thereby determining the deregulated 25 genes in the tumour; - providing a database comprising the target genes for each drug of the plurality of drugs; - determining a score for each drug of the plurality of drugs essentially based on the percentage of deregulated genes among the target genes for each drug in the tumour sample 30 from the patient, thereby a higher score is predictive of a higher relative efficacy of the drug for treating the tumour in the patient. Preferably, the normal sample is the normal histologic counterpart to the primary tumor.

WO 2011/003911 PCT/EP2010/059648 5 In particular, the step of characterizing molecular anomalies of a tumour sample comprises determining the genes differentially expressed in the tumour in comparison to the normal sample, and/or determining the gain or loss of gene copy number and/or detecting the presence of a mutation in a gene. Preferably, the step of characterizing molecular anomalies 5 of a tumour sample comprises determining a fold change (F) for the differentially expressed genes and/or for the gain or loss of gene copy number and, optionally, further determining the intensity of the gene transcription (Int) for the differentially expressed genes. Preferably, the target genes for each drug are classified in the database into the major target genes (MC), the minor target genes (Mc) and the resistance genes (CR). 10 In a first embodiment, the score (W) for a given drug is determined by the following algorithm: ( EC Fc> 2 ) W = P Z nCFc>2 wherein W is the score for the given drug; 15 P is the percentage of target genes for the given drug which are deregulated in the tumour of the patient; z is an optional multiplication coefficient associated to the presence of a mutation in a target gene of the given drug; E is sum; 20 Fc>2 is the fold change of each deregulated target gene for the given drug with a Fold Change higher than 2; nCFc> 2 refers to the number of target genes for the given drug with a Fold Change higher than 2. Preferably, Fc>2 is the Fold Change of each over-expressed target gene for the given 25 drug with a Fold Change higher than 2 and nCFe>2 is either the number of target genes for the given drug with a Fold Change higher than 2, or the number of over-expressed target genes for the given drug with a Fold Change higher than 2. In a second embodiment, the score (W) for a given drug is determined by the following algorithm: ( ( mCM FCM Cm FCm( YCR FCR W=nP q, z + q2Z2- q3Z3 30 1- n1CM n2Cm n3CR WO 2011/003911 PCT/EP2010/059648 6 wherein W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the tumour of the patient; 5 E is sum; CM refers to major target genes for the given drug; Cm refers to minor target genes for the given drug; CR refers to resistance genes for the given drug; n 1 CM, n 2 Cm and n 3 CR are respectively the number of deregulated target genes with a 10 defined threshold for major target genes, minor target genes and resistance genes; FcM, FCm and FCR are the Fold change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively; q1, q2 and q3 are optional multiplication coefficients for major target genes, minor target genes and resistance genes, respectively; 15 Z1, Z2 and Z3 are optional multiplication coefficients associated to the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively. In a third embodiment, the score (W) for a given drug is determined by the following algorithm: PCM( ECM FCM PCm( ECm FCm) PCR( YCR FCR) W = q, z, + q2 Z2 - q3 z 3 n 1 CM n 2 Cm n 3 CR 20 wherein the meaning of W, E, CM, Cm, CR, FCM, FCm, FCR, q, q2, q3, Z 1 , Z 2 and Z 3 are the same than the previous algorithm and PCM, PCm and PCR are the percentage of genes for the given drug which are deregulated in the tumour of the individual for major target genes, minor target genes and resistance genes, respectively In a fourth embodiment, the score (W) for a given drug is determined by one of the 25 following algorithms: ( CM F CM xIntM) MCm FCm x Intm) (YCR F CR X IntCR) W=P q, z + q2 Z2 q 3 Z3 nCM n 2 Cm n 3 CR or PCM(Y-CM FCM X IntCM) PCm(YCm FCm x Intcm) PCRQCR FCR X ntCR) VV= qz1 + q2 Z2- q 3 z 3 n 1 CM n 2 Cm n 3 C R WO 2011/003911 PCT/EP2010/059648 7 wherein the meaning of W, E, CM, Cm, CR, Fcm, Fcm, FCR, q1, q2, q3, Z 1 , Z 2 and z 3 , and if present PCM, Pcm and PCR, are the same than the previous algorithm and Intcm, Intcm and lntCR are the intensity for major target genes, minor target genes and resistance genes, respectively. 5 In a fifth embodiment, the score (W) for a given drug is determined by one of the following algorithms: ( ACM FCM YCR FCR) W=P q, z, q3 z 3 n 1 CM n 3 CR PCM( 1CM FCM) PCR( 1CR FCR W =q, z, q 3 Z3 n 1 CM n 3 CR or ( CM FCMx IntCM) ( CR FCR x ItCR) 10W=P n~mq, z,- n Rq 3Z3 or PCM (CM FCM x IntCM) PCR (CR FCR X IntCR) W = q, z, q 3 z 3 n 1 CM n 3 CR wherein the meaning of W, E, CM, CR, FcM, FCR, q1, q3, Z 1 and Z 3 , and if present PCM, PCR, Intcm, and IntCR are the same than the previous algorithms. 15 Preferably, in the second to fifth embodiment, Fcm, Fcm and FCR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and n 1 CM, n 2 Cm and n 3 CR are either the number of target genes for the given drug with the defined threshold, or the number of over-expressed target genes for the given drug with the defined threshold. More preferably, the defined threshold is a Fold change of at least 2 or higher than 20 2. In addition, in the second to fifth embodiment, multiplication coefficients for the target genes can be comprised between 10 and 1,000 for major target genes (qi), 0.1 and 10 for minor target genes (q2) and 10 to 1,000 for resistance genes (q3).

WO 2011/003911 PCT/EP2010/059648 8 Furthermore, in the second to fifth embodiment, multiplication coefficients associated to a mutation z 1 , z 2 and z 3 are 1 when no mutation exists and, depending on the functional impact of the mutation, can be comprised between 10 and 1,000. BRIEF DESCRIPTION OF THE DRAWINGS 5 Figure 1: Thoracic scanner in October 2005 of the patient of Example 1. Figure 2 Thoracic scanner in November 2008 of the patient of Example 1. Figure 3 Thoracic scanner in April 2009 of the patient of Example 1. Figure 4 Thoracic scanner in July 2009 of the patient of Example 1. Figure 5 Thoracic scanners in January and March 2010 of the patient of Example 3. 10 DETAILED DESCRIPTION OF THE INVENTION The present invention provides a new concept for selecting the most appropriate therapy at the individual level. The drug selection is based on the biologic characteristics of the tumor of the individual to be treated in comparison to a normal sample from the same individual. Based on a score essentially based on the percentage of deregulated target genes or 15 microRNAs for each drug, the relative efficacy of the drugs can be predicted for the individual in order to treat the specific tumour. General Concept It is at the level of the optimal therapeutic strategy choice that applies the object of the present invention. For selecting the most appropriate therapeutic strategy, the method of the 20 present invention is taking into account of, on one hand, the biological data of the tumor to be treated as a whole and, on the other hand, a plurality of drugs, preferably all existing drugs (either registered or in development). A score is determined for each drug based on the biological characteristics of the specific tumour to be treated for a given subject. This score makes it possible to order the various drugs in decreasing order for their potential efficacy. 25 The physician can use these scores to select the optimal drug or combination of drugs for the given subject. This approach, allowing the association of each drug with a score depending on the biological characteristics of the tumour, constitutes the basis of this invention. Therefore, this invention is likely to meet the present needs for personalized medicine. The method can be used both for registered drugs and for developed drugs (e.g., 30 temporary authorization of use or clinical trials).

WO 2011/003911 PCT/EP2010/059648 9 This concept thus consists in considering the choice of the therapy for the patient at the level of the individual depending on the intrinsic characteristics of his tumour and not on global results obtained from large groups of individuals. The present invention is based on the combination of three fundamental points, 5 detailed below, and which are combined together to optimize the choice of the strategy for each individual having a cancer: - The first point corresponds to the analysis, as exhaustive as possible, of the biological or gene anomalies (amplification, deletions, mutations, gene expression, microRNAs expression and the like) which characterize a given tumour for an individual. 10 - The second point consists in the identification of genes known to be in relation with a drug. - The third point corresponds to the establishment of the connection between each drug and the anomalies detected in the tumour to be treated in an individual. An algorithm has been indentified for calculating a score for each drug in consideration of the tumour 15 characteristics and the genes known to be in relation with a drug. The method of the invention will allow guiding the therapeutic choice because the available drugs will be ordered on their score basis, reflecting their potential therapeutic efficacy for the given tumour in an individual. One advantage of the present method is that the relative efficacy of a plurality of drugs 20 can be predicted for an individual without exposing the individual to drugs. By a plurality of drugs is intended at least or about 10, 20, 30, 40, 50 or 100 different drugs. Indeed, when a panel of drugs is available for treatment, one cannot be envisaged to try the treatment by each drug on the patient. The present method allows the consideration of all potential therapeutic strategy and the selection of the most appropriate for the patient. 25 Indeed, the scores for the plurality of drugs allow the determination of the relative efficacy of the plurality of drugs for treating the tumour of the considered individual. Indeed, a drug having a higher score than another drug is predicted to have a higher efficacy for treating the tumour. By treating is intended that the drug allows to stop or slow down the growth of the tumour, and/or to decrease the size of tumour even up to its disappearance. By 30 treating is also intended to avoid the metastasis, the recurrence or the relapse. Another advantage is that the method does not depend on a cancer type. The method of the invention can be used for any type of cancer including haematological tumour (e.g. leukemia, lymphoma) and bladder, breast, stomach, thyroid, prostate, testis, liver, pancreatic, WO 2011/003911 PCT/EP2010/059648 10 bone, pancreatic, kidney, endometrial, melanoma, lung, gastric, colorectal, prostate, head or neck tumours, brain, neuroblastoma, and ovarian cancer. In a preferred embodiment, the patient or individual is a human being. Tumour characterization 5 Tumour characterization corresponds to the analysis, as exhaustive as possible, of the biological or gene anomalies (amplification, deletions, mutations, gene expression and the like) which characterize a given tumour for an individual. In particular, the anomalies are determined in a tumour from the patient in comparison with a normal tissue of the same patient. Preferably, the tumour sample and the normal sample provides from the same type of 10 tissue. For the tumour characterization, several technologies are available and can be combined. The first technology is the gene analysis. This analysis can be carried out by CGH (Comparative Genomic Hybridization) which makes it possible to compare the tumoral DNA with the normal DNA of the same individual to detect chromosomal aberrations, i.e. the 15 chromosomal losses or gains. This technology is well-known by the man skilled in the art. As an illustration of this knowledge, the following reviews or reference books can be cited: Davies et al. (2005, Chromosome Research, 13, 237-248). This technology can also help to identify translocations. It can be easily carried out with frozen biopsies or tumoral paraffin included material. CGH results are expressed as the ratios of copy numbers in the tumoral 20 material and in normal tissue. A threshold of 0.5 is been acknowledged to describe a gain or a loss. More this ratio is high, more the amplitude of the anomaly is important. Thus, an important anomaly is likely to have a real impact at the biological level. However, the chromosomal aberrations only represent a weak part of the origins of gene expression deregulation. This is why other technologies are necessary. CGH have another advantage, to 25 certify presence of tumoral samples in the tumoral biopsy or biospecimen, and this whenever an aberration can be detected. The second technology allowing a functional genomic analysis corresponds to the measurement of mRNA and microRNA. The determination of the expression level variation for these RNA is carried out by comparing the expression levels in a tumoral tissue and in the 30 corresponding normal tissue. For instance, in case of colon adenocarcinoma, the corresponding normal tissue is the normal colic mucosal tissue. The gene expression analysis allows the study of the independent deregulations or deregulations due to chromosomal aberrations. Indeed, the regulation of the transformational activity of genes is complex and WO 2011/003911 PCT/EP2010/059648 11 involves many levels of regulation: trans/cis transcription factors, promoters, chromatin regulation, and the like. Generally, all deregulations (over-expression or under-expression) are considered with a ratio tumour/normal of at least 2. This threshold called "fold change" can thus have a positive value > 2 or a negative value < -2. The same concept applies to the 5 microRNAs which play an important role in the post-transcriptional regulation of genes, therefore for the proteins expression. Technologies that can be used comprise Northern analysis, mRNA or cDNA microarrays, RT-PCT (in particular quantitative RT-PCR) and the like. The level of transcription can be determined at the mRNA level or at the encoded protein level. Protein expression can be assessed by Western blotting, immunoassay, proteomics tools 10 or mass spectrometry. These two types of analyses, CGH and RNA expression determination can be supplemented by an analysis of the mutational status of genes. Indeed, the presence of mutation leading to a functional gain or loss has an important effect on biology of the tumour without being always connected to variations of gene expression or of gene copy number. 15 Many mutations are known to have a direct effect on the activity of a treatment by inducing increased sensitivities or resistances. For example, the mutations in the tyrosine kinase domain of EGFR are often associated with sensitivity to the small molecules inhibiting EGFR, the mutations in KRAS gene are associated with resistance to the treatment by monoclonal antibodies targeting EGFR. In addition to mutational status, some SNP can also 20 be detected. Indeed, SNP can be also associated to a functional gain or loss, a resistance or a toxicity for a drug. The mutational status can be determined by any method known in the art, for instance by sequencing, microsequencing or hybridization. In short, high throughput genomic technologies can be used to characterize in the most exhaustive possible way the biological anomalies of a given tumour from an individual to be 25 treated. The experimental data for each tumour are compiled in basic files being used for the application of the algorithms allowing calculation of a score for each drug. These files comprise the copy number of genes, the mutations, the fold-changes or the intensities of signals (proportional to the number of transcripts or to the number of gene copy) for normal tissue (Intensity 1 or I1) and for tumoral tissue (intensity 2 or 12). The functional genomic 30 analysis allows the simultaneous measurement of 44,000 or more (for example 244,000) RNA sequences covering all the genome. Preferably, a filtration can be applied to retain only the probes having a ratio or fold-changes higher or lower than 2 and whose average of the intensities II and 12 is higher than 100 units of fluorescence (arbitrary units).

WO 2011/003911 PCT/EP2010/059648 12 The term "molecular anomalies" refers herein to the gene expression differences (either mRNA, microRNA or protein expression), to a gain or loss of gene copy number, or to a mutation presence. In a particular embodiment of the invention, the exhaustive characterization of the 5 tumour is replaced by the characterization of the target genes of the drug database. In this embodiment, specific array can be prepared to determine the gene expression level of all the target genes of the database. Drug Database For the method of the invention, it is necessary to provide a database with a list of 10 target genes for each drug of the database. As explained before, a target gene for a drug can be, without to be limited thereto, any gene documented to be involved in the drug mechanism of action, to be involved in the drug metabolism, to have a modified gene expression in presence of the drug, to be associated with a drug resistance, to be associated with a drug toxicity. The database can be prepared based on the search in the public databases (such as 15 CTD, DrugBank, PubMed, and the like) in order to identify the genes associated with each drug. For instance, the database can be built based on the CTD (The Comparative Toxicogenomics Database, http://ctd.mdibLorg/) data for a selection of drugs and their molecular targets (genes), restricted to the human species (ID 9606). These data can be crossed with genes' information from LocusLink (gene symbol, RefSeq NM, gene 20 description). Finally, each drug / gene interaction in the database can be qualified from the available publications, to determine the type of interactions: some positive interactions (target, sensitivity, drug activator, drug carrier, toxicity reverser, ...), some negative interactions (resistance, toxicity, drug metabolism, apoptosis, death, ... ). The identified genes can have different roles and significances. Therefore, in a 25 preferred embodiment, the target genes are classified into three categories: the major target genes, the minor target genes and the resistance genes. The identification of these genes from the public data (public literatures and data banks) and their classification in the three categories form an integral part of this invention. The major target genes are those which have been demonstrated to have a clear cause and effect link with the drug mechanism of action. 30 For example, HER2 gene is regarded as major target gene for trastuzumab, VEGFA gene is regarded as major target gene for bevacizumab, and the like. A given drug can have one or more major target genes. This category also includes the genes known to be involved in the drug metabolization when drugs are known to become active only when an active metabolite WO 2011/003911 PCT/EP2010/059648 13 is generated. The minor target genes are those which are found to be those whose level of regulation is modified in the presence of the drug, without a direct link with the drug mechanism of action. The resistance genes comprise genes known to induce a direct resistance to the drug but also genes associated with a major toxicity. For example, ERCC1 5 gene is a target gene of resistance for the use of platinum salts. For example, some cytochrome P450 isoforms are associated with a major toxicity. In a particular embodiment of the invention, the considered target genes can only belong to the two following categories: the major target genes and the resistance genes. A first drug database has been established by the inventors and is disclosed in Table 1. 10 For some drugs, the target genes have been categorized. The drug database can be incremented over the time, by categorizing target genes for a drug, and/or by adding new drug, new target genes and/or by including combination data (e.g. combination of drugs with radiotherapy or combination of drugs). More complete is the drug database, more accurate is the prediction. However, the 15 method for predicting the relative efficacy of drugs can be carried out as soon as a preliminary database is ready. Algorithm An algorithm has been indentified for calculating a score for each drug in consideration of the tumour characteristics and the genes known to be in relation with a drug. 20 This calculation can be carried out by specific software by using of the scripts developed under R for instance, and allowing the determination of the frequencies and the association links between the file of target genes for the drugs and the file integrating the data of the genomic analysis resulting from the biological investigation from the tumour of the individual. 25 The algorithm can take into account the following parameter: 1) the whole percentage of deregulation of target genes of a drug. Therefore, the list of target genes for a given drug is compared with the list of deregulated genes in order to determine the percentage of deregulated genes for this drug. For instance, if 10 target genes have been identified for a given drug and, for a given tumour, 4 of the 10 target genes are 30 found to be deregulated, then the percentage of deregulated genes for this drug is 40 %. 2) the deregulation extent and sense (e.g., over- or under-expression) of the target genes defined by a Fold Change (Fc) and an average intensity (Avglnt). These parameters can WO 2011/003911 PCT/EP2010/059648 14 be defined either as a whole for the target genes or by each category (e.g., major target genes, minor target genes and resistance target genes). 3) the presence of mutations in target genes known to have an effect on the given drug. An algorithm is used to calculate a score for each drug of the database in consideration 5 of the tumour characterization for the subject to be treated. A first basic algorithm that can be used in the method is the following: ( EC Fc> 2 ) W = P Z nCFc>2 wherein W is the score for a given drug; 10 P is the percentage of target genes for the given drug which are deregulated in the tumour of the individual; z is an optional multiplication coefficient associated to the presence of a mutation in a target gene ; E is sum; 15 Fc>2 is the Fold Change of each deregulated target gene for a given drug with a Fold Change higher than 2; nCFe>2 refers to the number of target genes for the given drug with a Fold Change higher than 2. In a particular embodiment of this algorithm, Fc>2 is the Fold Change of each over 20 expressed target gene for a given drug with a Fold Change higher than 2 and nCFe> 2 can refer to the number of target genes for the given drug with a Fold Change higher than 2, or the number of over-expressed target genes for the given drug with a Fold Change higher than 2. Of course, the algorithm can be more complex in order to take into account the category of the target genes (e.g., major target gene, minor target gene or resistance target 25 gene), for instance by introducing a multiplication coefficient. Such a more complex algorithm can be the following: ( ECM FCM Cm FCm( ECR FCR W =P q, z, + -q 2 Z2 - q3 Z3 n1CM n 2 Cm n 3 CR wherein W is the score for a given drug; WO 2011/003911 PCT/EP2010/059648 15 P is the percentage of target genes for the given drug which are deregulated in the tumour of the individual; E is sum; CM refers to major target gene for the given drug; 5 Cm refers to minor target gene for the given drug; CR refers to resistance gene for the given drug; n 1 CM, n 2 Cm and n 3 CR respectively are the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes; Fcm, Fcm and FCR are the Fold change of each gene higher than the defined threshold 10 for major target genes, minor target genes and resistance genes, respectively; q1, q2 and q3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively; z 1 , z 2 and z 3 are optional multiplication coefficients associated to the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively. 15 For instance, multiplication coefficients for the target genes can be comprised between 10 and 1,000 for major target genes, 0.1 and 10 for minor target genes and 10 to 1,000 for resistance genes. Other values for multiplication coefficients are not excluded. The multiplication coefficients associated to a mutation are 1 when no mutation exists. Depending on the functional impact of the mutation, the coefficient z can be comprised 20 between 10 and 1,000, for instance. Other values for multiplication coefficients associated to a mutation are not excluded. In a preferred embodiment, the defined threshold is a Fold change of at least 2 or higher than 2. However, the consideration of a lower threshold is not excluded in the present method since a fold change of 1.5 can be significant for some genes. 25 In a particular embodiment, FCM, Fem and FCR can be the Fold Change of each over expressed target gene for the given drug with the defined threshold and n 1 CM, n 2 Cm and n 3 CR can refer to the number of target genes for the given drug with the defined threshold, or the number of over-expressed target genes for the given drug with the defined threshold. In an alternative complex algorithm, the formulae can be the following: PCM( ECM FCM) PCm( LCm FCm) PCR( ECR FCR) W q, z, + q2 Z2 q 3 z 3 30 n 1 CM n 2 Cm n 3 CR wherein the meaning of W, E, CM, Cm, CR, FCM, Fcm, FCR, q1, q2, q3, Z 1 , Z 2 and Z 3 are the same than the previous algorithm and PCM, PCm and PCR are the percentage of genes for the WO 2011/003911 PCT/EP2010/059648 16 given drug which are deregulated in the tumour of the individual for major target genes, minor target genes and resistance genes, respectively. Similarly, in a preferred embodiment, the defined threshold is a Fold change of at least 2 or higher than 2. However, the consideration of a lower threshold is not excluded in the 5 present method since a fold change of 1.5 can be significant for some genes. In a particular embodiment, FCM, FCm and FCR can be the Fold Change of each over expressed target gene for the given drug with the defined threshold and n 1 CM, n 2 Cm and n 3 CR can refer to the number of target genes for the given drug with the defined threshold, or the number of over-expressed target genes for the given drug with the defined threshold. 10 In a particular embodiment, the algorithm can take into account the average intensity or intensity variation. This parameter is indicative of the transcription level of genes. Indeed, it can be considered that for a same Fold Change of 2, a gene deregulation can have a different weight depending on the intensity of the transcription, for instance 200/100 in comparison to 200,000/100,000. 15 Accordingly, a still more complex algorithm can be one of the followings: (CM FCM X Intm) ( Cm FCm x intCm) ( YCR FCR X IntR) W=P q, z, + q2 Z2 - 3 z 3 nCM n 2 Cm n 3 CR PCM(Y-CM FCM X IntCM) PCmQYCm FCm x IntCm) PCRQCR FCR X CnteR) VV= qz1+ q2 Z2- q 3 z 3 n 1 CM n 2 Cm n 3 C R wherein the meaning of W, E, CM, Cm, CR, FcM, FCm, FCR, q1, q2, q3, Zi, Z2 and Z3, and if present PCM, PCm and PCR, are the same than the previous algorithm and IntcM, Intcm and 20 IntCR are the intensity for major target genes, minor target genes and resistance genes, respectively. "Int" can be the intensity of the gene transcription in the tumour sample, the difference of the gene transcription between the tumour sample and the normal sample from the individual. In an additional embodiment, the method can be focused on the major target genes and 25 the resistance genes, without taking into account of the minor target genes. In this embodiment, the algorithm could be one of the followings: (-CM FCM ( CR FCR) W=P q, z, - 3 z 3 n 1 CM n 3

CR

WO 2011/003911 PCT/EP2010/059648 17 PCM( 1CM FCM) PCR( 1CR FCR W = q, z, q 3 z 3 n 1 CM n 3 CR ( ACM FCMx IntCM) ( Y'CR FCR X IfltCR) n 1 CM n 3 CR PCM QCM FCMx IfntCM) PCR CR FCR X IntCR) W = q, z, q 3 z 3 n 1 CM n 3 CR wherein the meaning of W, E, CM, CR, FCM, FCR, q1, q3, ZI and Z 3 , and if present PCM, 5 PCR, IntcM, and IntCR are the same than the previous algorithms. Preferably, the selected algorithm is validated with two models: a retrospective model (e.g., tumours for which chemotherapies have been performed and for which the response to treatments is known) ; and a prospective model allowing the evaluation of the efficacy of a particular treatment in relation with the score. 10 During the algorithm validation tests, some variables can be refined, in particular the multiplication coefficients, the consideration of the average intensity or not, the threshold of the fold change. In addition, during this step, one can detennine if it is preferable to use CGH or functional genomic analysis or both. The method also considers other variants of the algorithm that could be proposed, the 15 final objective staying to calculate a score for each drug based on the characteristic of the tumour of the individual to be treated, in particular on the biologic and genetic anomalies of the tumour. Further aspects and advantages of this invention will be disclosed in the following examples, which should be regarded as illustrative and not limiting the scope of this 20 invention. EXAMPLES Example 1 At diagnosis, 70% of lung cancers are in late stages. They are non operable with a poor clinical outcome. 25 The method of the present invention has been used in a patient case to help the practitioner to choose the most appropriate treatment.

WO 2011/003911 PCT/EP2010/059648 18 The patient was a male Caucasian of 58 years old. He suffered of a non-small cell lung carcinoma (NSCLC), cT4, NO, M1. Nine therapeutic lines have been used, namely cisplatin Gemzar, taxotere, navelbine, taxol-carboplatin, mediastinal radiotherapy, IRESSA, alimta, tarceva and HKI 272 (pan Her inhibitor). For HKI 272, the patient has been included in a 5 clinical trial. HKI 272 began in October 2005. Figure 1 show NMR of mediastinal lymph node (C1) and adrenal node (C2). HKI was efficient; the patient remained in the study almost three years. However, after 37 months of HKI 272, a disease progression was observed (Figure 2). New subclavious metastasis appeared. This is one of the biggest problems in oncology today, 10 even if there is initial response, a secondary resistance to treatment often occurs. Accordingly, the decision to stop HKI 272 treatment was taken since new metastasis occured. It is important to mention that practicioners considered at that time that HKI 272 was the last therapeutic lines available for this patient. Subclavious metastasis was resected and used for complete molecular profiling. The 15 features of the profiling were: 1- comparison tumoral tissue versus normal lung tissue (T vs N); 2- Comparative genome hybridzation (CGH) (T vs N); 3- Gene expression (GE) comparison (T vs N); 4- microRNA (miRNA) profiling (T vs N); 20 5- Sequencing of genes including EGFR, p5 3 , CTNNB1, AKT1, BRAF, KRAS, HRAS, NRAS, PIK3CA, FBXW7, EGFR, ERBB2, KIT, NOTCH, PTEN, STK11, TP53, APC, MET, RB1, FGFR2, FGFR3, JAK2, TSC1, TSC2, CDKN2A, CDKN2A, TOPI, TOP2A, PDGFRA, VHL, CDK4, JAKI, TYK. 6- No relevant mutations were found in any of these genes (for example no mutation 25 of EGFR or other genes). Therefore mutations did not impact algortitm for this patient. Only relevant results obtained with gene expression were used. Then, the algorithm of the present invention was applied on these data in order to predict the drug efficacy. A score for each drug was calculated based on the collected data. The algorithm used was the following: ( Ec

F>

2 ) W = P z 30 ncFc>2 whith W is the score for a given drug; WO 2011/003911 PCT/EP2010/059648 19 P is the percentage of target genes for the given drug which are deregulated in the tumour of the individual; z is 1 because, in this example, no mutation was detected; E- is sum; 5 Fc>2 is the Fold Change of each deregulated target gene for a given drug with a Fold Change higher than 2; nCFe>2 refers to the number of target genes for the given drug with a Fold Change higher than 2. Table 2 shows the calculated scores. It can be observed that the drugs used in the 10 previous therapeutic lines were associated with low scores, namely 108 for cisplatine, 70 for gemzar, 77 for taxotere, 147 for taxol, 82 for carboplatin, 66 for Iressa, and 73 for Alimta. On December 2008, HKI 272 was stopped and a treatment with a combination of Xeloda (3 600 mg/day, from Day 1 to Day 14, every 21 days) and Lapatinib (1 250 mg/day) began. At the beginning of this treatment, the patient showed a rapid disease progression and 15 demonstrated a degraded health. Lapatinib, an anti-HERI and -HER2 inhibitor, was justified, even if no mutation was detected in EGFR, because EGF overexpressed 15 folds in the patient and it was needed to continue HKI 272. Indeed, overexpression of EGF in the tumor induces a constant activation of EGFR, that is why it appeared logic to assure the transition from HK1272 to another anti EGFR in order to cover the same spectra. Xeloda (score 555) was 20 selected based on algorithm's score. The disease was stable but recurrential paralysis was observed. Accordingly, in February 2009, it was decided to add Thiotepa which showed the highest algorithm score (score 713). After two months of treatment with the combination Xeloda (3 600 mg/day, 5 days by week, 3 weeks on 4 weeks), Lapatinib (1 250 mg/day) and Thiotepa (15-30 mg/day, 25 Days 1 and 2, every 4 weeks), the disease was stable (Figure 3). The disease was still stable for eleven months (Figure 4), no more recurrential paralysis occurred and the patient showed a good general status. In addition, the present method allows the determination of future therapeutic combinations. Indeed, during cancer treatment, resistance often appears. At least three others 30 drugs showed high scores and can be used in case of resistance to the combination Xeloda, Lapatinib and Thiotepa, namely fotemustine (score 627), rituximab (score 761) and trabectidin (score 376). By using the present method for selecting drugs, unexpected results were obtained. Indeed, without the score predicting the potential efficacy of a drug for a particular patient, WO 2011/003911 PCT/EP2010/059648 20 the practitioner would not select both Xeloda and Thiotepa. Indeed, there is no indication for these drugs in lung cancer, in particular NSCLC. The present method allowed reaching fourteen months of stability with a good general status for the patient, whereas the vital prognosis was only of few weeks at the initiation of the treatment with the combination of 5 Xeloda and Thiotepa. In conclusion, the present example proves the value of the method of the present invention to help the practitioner to select appropriate drugs based on the individual data. Retrospectively, the use of the new predicting method clearly demonstrated that all previous therapeutic lines, totaly inefficient, were associated with a very low predictive score, 10 as described. This is exactly the purpose of this innovative method, to be able to provide a predictive determination of the efficacy of drugs, and in this example, there is perfect validation of the concept, since all used inefficient drugs are linked with low score. Other patients experiment the new procedure, and the high added value of the present method is to demonstrate that each patient needs a unique combination of drugs. The method 15 appears therefore extremely relevant in the area of individualized selection of treatments. Example 2 The patient was 64 years old. He suffered of a bronchial adenocarcinoma T4 with bones and pleural metastases. Two therapeutic lines have been used, namely cisplatin-Alimta and Tarceva. The first therapeutic line was associated with disease progression and the second 20 was inefficient and led to a rapid progression. A biopsy of normal bronchial mucosa and a tumoral biopsy were carried out and used for mutational analysis by sequencing, CGH, microRNAs analysis and Genome expression analysis. CGH profile comprised numerous alteration (loss or gain), proving the tumoral status 25 of the biopsy. Mutational analysis including the genes as listed in Example 1 results in the identification of a mutation G464V in BRAF gene (B-Raf proto-oncogene serine/threonine protein kinase, GenelD 673). This mutation is postulated to be activating with intrinsic mitotic signalisation. Therefore, a treatment with sorafenib could be contemplated. 30 Based on Genome Expression analysis, scores have been calculated as detailed in Example 1 and are shown in the following table only for some relevant drugs.

WO 2011/003911 PCT/EP2010/059648 21 ID Chemical Target Genes List Found s-. (DTargets -6.C TIPIFARNIB 4ABCB1 CYP3A4 CYP3A5 2 CYP3A4 (17.99) 4 ~~ ~CPA (17.99).3 0.3 03 (Zarnestra) UGT1A1 (0) CYP3A5 (23.46) (50.0%) BEVACIZUMAB VEGFA VEGFA (2.30, 7.27, 4 58 4 58 457 (A vastin) 1(1+0) i4 15) (100 0%) AFLIBERCEPT 1 VEGFA VEGFA (2 30, 7.27, 4 58 4 58 457 (VEGE Trap) (+) 415) (100.0%) IFOSFAMIDE (Cyfos, Holoxan, BCYP3A4 (17.99) 1000, IFEX CYP2 CP3A4 CYPA5 4 CYP3A5 (23.46) CYP2B361 Y34 Y3 Ifex/Mesnex Kit, 11 (2+2) 14.82 20 73 376 Ifosfamide/Mesna Kit, G.1%GST P1BCL2 (442) CYP2B6 /soendoxan, Mitoxana, ( 13.41) Naxamide) ECTEINASCIDIN 743 3 (Trabectedin, ET-743, 4 CCNA2 CCNB1 CCNB2 E2F1 (3+0) CCNA2 (4.55) CCNB1 4 24 4 24 317 Yondelis) (750 I%) (2.95) CCNB2 (5.21) VINORELBINE CASP3 CDKN2A PTGS2 3 PTGS2 (17.97) :SLC29A1 (2.36) (Navelbine, Navelbine 7 RALBP1 RBM17 SLC29A1 (2+1) 7.46 10 16 290 Base) TUBB2A (42 9%) TUBB2A (-2.05) DHFR FAS FPGS GART GGH 4 DHFR (2.40, 2.26) PMTa) 10 RBM17 SLC19A1 TP53 TYMP (4+0) GART (3.29) SLC19A1 3.91 3.91 156 TYMS (40 0%) (4 68) TYMS (5.32) ABCG2 ADORA1 AKT1 AREG AVEN CASP3 CGRRF1 AREG (6.94) CYP3A4 COL4A3BP CORO1C CYP1A2 (17.99) CYP3A5 (23.46) CYP2C19 CYP2C9 CYP2D6 DUSP3 (3.18) EPOR CYP2F1 CYP3A4 CYP3A5 (2.02, 2.11) IF16 (3.79) DUSP3 DUSP9 E2F1 EGF NPTX2 (2.77) PHLDA2 GEFITINIB EGFR EPOR EPS15 ERBB2 18 (5.98) PLBD1 (8.04) SKI (Iressa, Irressat, 57 EREG ESR1 FGF6 GADD45A (11+7) (2.09) TYMS (5.32) 6.69 7.42 143 Tarceva) GADD45G GARS GCLC GNB2 (31 6%) GUCY2D HBEGF IF16 IGFBP3 CYP2C9 (-2.94) CYP2F1 IL8 LEPR MAPK1 MAPK3 MLH1 (-27.19, -7.80) EGF ( NFKB1 NPTX2 NRL OSMR 2 04) EGFR (-3.99) PARP1 PHLDA2 PLBD1 PTEN GADD45G (-3.12) GCLC QSOX1 RBM7 RPA1 SFN SKI (-2 59) LEPR (-6.56) TGFA TNFRSF1 B TYMS CISPLATIN A2M ABCB1 ABC ABCC2 137 ABHD2 (2 30, 2.58) (Abiplatin, 403 ABCC5 ABHD1 1 ABHD2 ABR (63+71+3) ADAM10 (2.01) BACH1 5.94 5.17 80 Biocisplatinum, ACSL3 ADAM10 ADFP (34.0%) (2.09) BAX (2.02) BBC3 WO 2011/003911 PCT/EP2010/059648 22 Briplatin, Carboquone, ADORA2B AHCYL. AKAP12 . . (2.35) BCAM (2.00) Cis Pt 11, Cismaplat, AKR1B1 AKT1 AKT2 AKT3 ALB BIRC2 (2.06) BIRC3 Cisplatine, Cisplaty, ANXA1 APEX1 ARF6 ARHGEF6 (4.00) BIRC5 (12.70) Citoplationo, ARID5B ARMCX2 ASS1 ASTN1 CCNE1 (3.18) CDKN1A Lederplatin, Neoplatin, ATP6V1G2 AXL B3GALT4 (2.33) CILP (4.39) Plastin, Platamine, BACH1 BAX BBC3 BCAM CNTNAP2 (20.81) Platiblastin, Platidiam, BCAS4 BCL2 BCL2L1 BCL2L12 CYP3A4 (17.99) DAB2 Platinex, Platinol, BIRC2 BIRC3 BIRC5 BMP7 (2.33) DKK1 (51.55) Platino-AQ, BTG2 C110RF68 C11ORF9 ETV4 (2.96) FADS1 Platinoxan, Randa) C190RF2 C40RF29 C70RF16 (2.31, 2.17) FANCC CA2 CALCB CASP2 CASP3 (2.05) FEN1 (2.99, 3.13) CASP8 CASP9 CAT CAV1 FOS (32.01) GDF15 CCDC85B CCNE1 CCNG2 (14.33) GLRX2 (2.53) CD151 CD55 CDC40 CDH3 GSTM3 (2.04) HHEX CDKL5 CDKN1A CDKN2AIP (3.59) HPCAL1 (2.96) CELSR2 CES2 CFHR1 CFLAR IF130 (2.79) JUN (2.41) CIAPIN1 CIDEB CILP CKMT1B KLK3 (2.11) LANCL1 CLU CNTF CNTNAP2 COLl 1A2 (2.60) LAPTM4B (3.54, COL4A5 CORO1C CREBBP 3.75) MAP2K6 (2.32) CTAGE4 CTDSP1 CTH MED21 (2.11) MEST CTNNAL1 CYCS CYP2C9 (8.01) MMP15 (3.00) CYP3A4 D6S2723E DAB2 MMP16 (3.26) NCOA3 DDIT3 DDR1 DENND4A (2.54) NMI (2.13) NMU DEPDC6 DIABLO DKK1 DMBT1 (2.49) NR4A1 (3.47) DPYD DRAP1 DST EDN2 NUDT1 (2.01) PDXK EDNRA EGFR ELMO2 EMP3 (3.48, 2.73, 2.41) ENPP2 EP300 ERBB2 ERCC1 PMAIP1 (3.68) PRKCI ERCC2 ETV4 F8A3 FADD (2.83, 2.64) PTGS2 FADS1 FAM129AFAM13A1 (17.97) PXDN (3.61, FAM46A FANCC FANCG FAS 4.00) RNASET2 (2.15) FASLG FASN FEN1 FGF7 RNF34 (2.28) SDC2 FGF9 FGFR2 FGFR3 FKBP2 (3.25) SLC29A1 (2.36) FMOD FOS FOSLI FOXCl SP1 (2.15, 2.24) SPON1 FTLL1 GADD45A GALNT7 (2.57) STEAP1 (2.90, GARS GAS1 GCLC GCLM 3.34, 2.83) STYX (2.41) GCNT1 GCNT2 GDF15 GLRX2 TANK (2.02) TERT GMPPA GOLGA8A GOLGA8B (2.54) TP5313 (3.40) GOLSYN GPAA1 GPX3 GSTM1 TP11 (2.88) TRAM1 GSTM2 GSTM3 GSTO1 GSTP1 (2.56) TRIB3 (2.05, 2.13) GSTT1 GUCY1 B3 GUK1 HERV- TYMS (5.32) UPK1 B FRD HHEX HIF1A HLA-A HLA- (2.08) VEGFA (2.30, DPA3 HLA-G HNRNPA1 7.27,4.15) HPCAL1 HSD17B8 HSPB1 HSPE1 HTRA2 ICAM1 ID4 IF130 A2M (-7.98) ABCC5 ( IFITM1 IGFBP3 IGFBP5 2.30, -2.38) ANXA1 ( IL13RA1 IL1B ILIR1 IL4R 1L6 2.14) ARHGEF6 (-2.10) ITGA9 ITGAE ITGB4 ITPA JUN ARID5B (-2.58, -2.20, KCNA5 KCNK1 KLF2 KLK1 2.04) BCL2 (-4.42) BTG2 KLK10 KLK11 KLK12 KLK15 (-2.33) CA2 (-3.62) KLK2 KLK3 KLK4 KLK5 KLK6 CDH3 (-4.12) CELSR2 ( KLK7 KLK8 KLK9 KRT17 2.39) CES2 (-2.61) KRT19 KRT4 LANCL1 CIDEB (-2.19) CLU ( LAPTM4B LASS4 LEPRELl 8.31) COL4A5 (-2.74) LIMCH1 LOH11CR2ALTBP1 CTNNAL1 (-4.41) M6PRBP1 MACF1 MAD2L2 CYP2C9 (-2.94) DDR1 ( MAGED2 MAL MAP2K6 MCM2 2.50, -2.46) DMBT1 ( MED1 MED21 MEST MGMT 31.82) DPYD (-4.02, MLL MMP10 MMP15 MMP16 3.55) DST (-3.08, -14.93) MMP3 MPO MPP2 MRPS27 EGFR (-3.99) FAM129A MT1 AMT1 FMT2A MT3 MVP ~.(-11.54, -14.34) FGF7 ( WO 2011/003911 PCT/EP2010/059648 23 MYC NAB1 NAIP NCOA3 .6.10, -6.45, -2.30) FGF9 NDUFS8 NEAT1 NEDD9 NMI (-2.12) FGFR2 (-15.60, NMU NOTCH2 NOTCH4 NR112 8.81) FGFR3 (-3.11, NR4A1 NTHL1 NUDT1 OPTN 7.43) FMOD (-2.27) P4HA2 PAFAH1B3 PAPPA2 FOXC1 (-3.90, -4.46) PARD6A PARP1 PAX8 PBX1 GAS1 (-2.36) GCLC ( PCNA PDIA3 PDXK PFDN5 2.59) GCLM (-3.92) PFKFB4 PFKP PGK1 PHIP GOLGA8A (-3.47, -2.17) PHLDA1 PLP2 PMAIP1 PMPCA GOLSYN (-3.18) GSTM2 PMS2L1 1 POLA1 POP4 PPIE (-3.53) HLA-A (-2.18) PPP1R1B PPP3CB PRKCI ID4 (-2.25) ITGA9 (-2.61) PRKDC PRNP PRODH PROS1 KLK10 (.5.32, -12.20) PSMB10 PTEN PTGS1 PTGS2 KLK11 (.51.60) KLK12 ( PTK2 PXDN QSOX1 RAB40B 21.92) KRT19 (-2.14) RAD21 RALB RALBP1 RASSF1 KRT4 (-11.12) LASS4 ( RB1 RBCK1 RBM9 RBP1 RELA 2.24) LIMCH1 (-2.87) RHOC RING RNASET2 RNF34 LTBP1 (-2.35) MACF1 ( RPL21 RPL36 RPL6 RPS14 2.02) MAL (-12.81) RPS18 RPS4Y2 RPS5 RRAGD MMP1O (.62.97) MT3 ( RUNX3 RXRB SCRN1 SDC2 13.83, -2.32) NAIP ( SERPINB4 SERPINE2 SFN 2.67) PBX1 (-3.32, -3.26) SH2B3 SH3BGRL3 SLC15A1 PHIP (-2.01) PHLDA1 ( SLC20A1 SLC29A1 SLC2A1 3.12, -2.49) PPP1R1B ( SLC31A1 SLC39A7 SLC6A12 3.30) PTGS1 (-4.87) SLC7A11 SLC7A8 SLPI RRAGD (-2.48) RUNX3 SNAP29 SNAPC3 SNRPA (-4.02) SERPINB4 ( SOCS1 SOCS2 SOD2 SOD3 7.72) SLC31A1 (-2.78, SP1 SP100 SP110 SPINT2 2.75, -2.79) SLPI ( SPON1 SPTLC2 STEAP1 STYX 10.90, -12.82) SOD3 ( SULT1A1 SULT1A2 SWAP70 5.07) SWAP70 (-2.00) SYNJ2 TANK TERT TF TFAP2A TF (4.25, -4.55) TFB1M TGFA TGFB1 TLR2 TNFSF10 (-3.01) TMSL6 TNF TNFAIP3 TNFSF10 TUBB2A (-2.05) UCP2 ( TNFSF13 TP53 TP5313 TP73 2.53) UGT1A6 (-16.01, TPI1 TRADD TRAM1 TRAP1 24.90) VAV3 (-7.76) TRIB3 TRIP13 TSPAN12 WFDC2 (-14.33, -12.71) TUBA1A TUBB2A TXN XPA (-2.62) XRCC1 ( TXNDC13 TXNRD1 TYMP 2.80) TYMS UCP2 UGDH UGT1A1 UGT1A3 UGT1A6 UMPS BCAS4 (2.21, -2.07) UPK1B USP14 VAPA VAV3 CYCS (-2.70, 2.03) VEGFA VPS52 WDR46 WFDC2 PRNP (-4.40, 2.43) WWC1 XIAP XPA XRCC1 XRCC5 XRCC6 YARS ZFP36L1 ZFP36L2 ZNF192 ZNRD1 Accordingly, it can be observed that the drugs used in the two therapeutic lines, which did not provide therapeutic afficacy are associated with low scores, namely 80 for cisplatine, 156 for alimta and 143 for Tarceva. Accordingly, the method of the invention should avoid the choice of such treatments. 5 Since January 2010, Vinorelbine, associated with a score of 290, has been selected for treating the patient. At the beginning, the patient has a very worsened general status. Three months later, the disease was stable, then progressed. In conclusion, although the drug WO 2011/003911 PCT/EP2010/059648 24 associated with the best score has not been selected, the selected drug showed efficacy, conferring three months survival. Example 3 A patient was diagnosized in May 2007 for a primary bronchial adenocarcinoma with 5 bilateral pulmonary metastases and asymptomatic cerebral metastases. A surgical treatment has been carried out in November 2008 and two therapeutic lines have been used, namely thirteen cycles of cisplatin-gemcitabin for the first line and Alimta for the second one. The first line was associated with a partial response followed by a disease progression and the second line was only associated with a disease progression. 10 A normal bronchial biopsy and a pulmonary metastasis biopsy were carried out and used for mutational analysis by sequencing, CGH, microRNAs analysis and Genome expression analysis. CGH profile comprised numerous aberrations (loss and gain throughout the genome) demonstrating the tumoral status of the biopsy. 15 Mutational analysis including the genes as listed in Example 1 did not lead to the identification of any mutation. Based on Genome Expression analysis, scores have been calculated as detailed in Example 1 and are shown in the following table only for some relevant drugs. C )LL CD Chemical Target Genes List Found Targets List (with fold-changes) CO :ALB (84.71) BA)( (2.03) CARBOPLATIN ALB BAX BCL2 BCL2L12 BIRC2 6 ALB (81) BAX4(2803) (Paraplatin 16 CASP3 CASP9 DCT DPYD ERBB2 FAS (3+3) A1P3 (-26) DPYD(559 Paraplatin-AQ) PARP1 PCNA PTGS2 RBM17 TYMS (375%) 2.07) FAS (-2.69) BEVACIZUMABVEGIFA (3.18, 3.55, 4. 81, BEVACZUVAB 1 VEGFA (1+0) 3 81384 384 384 (A vastin) 384.34238 (100.0%) AFLIBERCEPT VEG FA (3.18, 3.55, 4.81, 1 .VEGFA (1+0) :3.84 :3,84 384. (VEGF Trap) (10.%)3.82) (100.0%) CERAn)B 2 FLT1 FLT4 (1+0) FLT1 (4.36, 5.59) 4.98 4.98 248 (Recentin)(50% (50.0%) 3 FLT1 (4.36, 5.59) AXITINIB 6 FLT1 FLT4 KDR KIT PDGFRA PDGFRB (3+0) PDGFRA (4.21, 3.56) 4 68 4 68 234 (50.0%) PDGFRB (5.18) CAMPTOTHECIN 57 ABCB1 AGRN ALB ANXA4 BAX BCL2 16 AGRN (2.26, 2.29, 2.11) 8.40 14.18 199 WO 2011/003911 PCT/EP2010/059648 25 BID BIRC5 BRCA1 BRCA2 CAB39 (8+8) ALB (84.71) BAX (2.03) CASP2 CASP3 CCNB1 CDK2 CDKN1A (28.1%) LGALS1 (3.09) PLK3 CEACAM1 CEBPZ CTSB CYCS DPYD (2. 10) PPP1R1B (5.31) E124 EP300 EPM2AIP1 FDXR GSTP1 THBS1 (10.46, 9.80) HNRNPC IL1B 1L8 JUN LGALS1 VEGFA (3.18, 3.55, 4.81 MAP2K3 MAP3K5 MAPK9 MAPT 3.82) MDM2 NCK2 PARP1 PLK3 PPP1R1B PRC1 RAD51 RB1 RBL1 RBL2 SDC1 ABCB1 (-2.74) CASP3 TAP1 TAX1BP3 THBS1 TNFSF9 TOP1 2.56) DPYD (-2.07) FDXR TP53 TP5313 TP53TG1 TYMS VEGFA (-4.08) GSTP1 (-2 04) XIAP MDM2 (-2.46) TAP1 2.71) TP53TG1 (-2 28) CDKN2A (9.83, 6.61) VINORELBINE CASP3 CDKN2A PTGS2 RLBP1 4 PTGS2 (2.81) TUBB2A (Navebine, 7 (3+1) (2.88) 4.12 4.64 198 Navebine Base) (57.1%) CASP3 (-2.56) ANTXR1 (2.23, 2 64, 3 53) AGPAT2 ALDH1A3 ALDH6A1 ANAPC1 BAX (2.03) CLIC4 (2 63, ANK3 ANKRD1 ANTXR1 ARF1 ATF3 2.54, 2.54) CTHRC1 BAIAP1 BAX BCL2 BCL2L1 BCL2L1 1 (19.05) DLG2 (3.80) ETV1 BCL6 BIRC2 BIRC3 BIRC5 BNIP3L (3.09, 3.19) GAL3ST1 C150RF15 C10RF144 C40RF18 (5.88) GAS1 (2.17) GPC3 CASP3 CCL20 CCND1 CCNG2 CD40 (3.00) HOXB2 (2.95, 2 38) CDA CDH1 CDKN1A CEBPB CKB IFIT1 (2.11) 1L6 (2.67) CLIC4 CMPK COX3 CSRP2 CTHRC1 JAM3 (2.13, 2.01) KLF8 CXCL1 CXCL2 CXCL3 CYP24A1 DDIT4 (3.81) KRT8 (2.56, 2 55) DHX9 DLG2 DNCH1 DPYD EDN2 MAF (2 66, 2.91) NEDD9 EGFR EGR1 ETV1 EV12B FBXO25 (3.66) NEU1 (2.80) NNMT FGG FOSL2 FXYD3 GAL3ST1 GAS1 (3.15) NRP1 (2.03, 300, GEMCITABINE GLRX GPC3 GTF2A1 HDAC9 HIF1A 2.60) PLAU (9.25) RGS2 (DDFC DFDC, HIST1H4B HIST1H4C HOXB2 HSPA1L (3.75) SCD (3.76, 3 04, GEO, Gemcin, HSPA5 IFIT1 1L6 INSIG1 IQGAP1 3.90) SLC2A3 (4.22, 7.94) Gemcitabina, IRAK2 IRF3 ITSN2 JAM3 KCTD12 KLF8 48 SPP1 (70.93) Gemcitabine, 158 KRT8 LARP5 LIFR LRRC28 LYZ MAF (31+17) TNFRSF11B (12.11, 8.17) 6.60 6.60 129 HCI, Gemcitabine MAGEA10 MAGEC1 MAP17 MARCKS (30.4%) TYRP1 (13.30) VEGFA hydrochloride, MARS MDM2 MGMT MX11 MYB (3.18, 3.55, 4.81, 3.82) Gemcitabinum, NCBP1 NEDD9 NEU1 NFKB2 NFKBIE WBP5 (2.13) ZNF274 Gemtro, Gemzar) NNMT NPEPPS NR4A3 NRG1 NRP1 (3.73) ZNF521 (2 79) P2RY5 PAQR8 PARP1 PCSK9 PDPK1 PJA2 PLAU PPFIA4 PPP1R15A PSAT1 ANK3 (-3.59, -3.38, -3.62) PTEN PTGS1 PTPN11 RAB8A RBM17 BIRC3 (-2.27) CASP3 RELA RGS2 RNF149 RPL37 RRAGD 2.56) CCL20 (-10 90) RRM1 SAT SCD SLC25A29 SLC29A1 CD40 (-2.36) CXCL1 SLC2A14 SLC2A3 SLC43A3 SLITRK2 47.43) CXCL2 (-2 14, SPP1 SYNCRIP TCOF1 TERT TGFB2 5.83) CXCL3 (-4.04) TNF TNFAIP3 TNFRSF11B TP53 CYP24A1 (-2.10) DPYD TP53BP1 TPS31NP1 TRAE1 TUBE1 2.07) FXYD3 (-2.79) TYMP TYMS TYRP1 UBE2C UGGT2 HDAC9 (-4.09) LYZ VEGFA VGF VLDLR WBP5 WNT5A 7.83) MDM2 (-2.46) XIAP ZNF274 ZNF449 ZNF521 MGMT (-2.34) MYB 8.21) NRG1 (-2.47, -336) CISPLATIN A2M ABCB1 ABCC1 ABCC2 ABCC5 A2M (2.45) ABCC2 (4 03, (Abiplatin, ABHD11 ABHD2 ABRACSL3 ADAM10 3.37) AKAP12 (2 40) Biocisplatinum, 403 ADFP ADORA2B AHCYL1 AKAP12 AKT3 (3. 10) ALB (84 71) 5 94 4 76 80 Briplatin, AKR1B1 AKT1 AKT2 AKT3 ALB ANXA1 (68%) BAX (2.03) CA2 (2.50) Carboquone, Cis APEX1 ARF6 ARHGEF6 ARID5B (8 CCNE1 (3.72) CILP (3 81) Pt II, Cismaplat, ARMCX2 ASS1 ASTN1 ATP6V1G2 AXL COL11A2 (21.07) DAB2 WO 2011/003911 PCT/EP2010/059648 26 Cisplatine, B3GALT4 BACH1 BAX BBC3 BCAM .(2.64) DKK1 (6.38) Cisplatyl, BCAS4 BCL2 BCL2L1 BCL2L12 BIRC2 EDNRA (2.53, 2.37) Citoplationo, BIRC3 BIRC5 BMP7 BTG2 C11ORF68 ENPP2 (2.36) ERCC2 Lederplatin, C11ORF9 C19ORF2 C40RF29 (2.03) FADS1 (3.44, 3.92) Neoplatin, C7ORF16 CA2 CALCB CASP2 CASP3 FANCC (2.22) FASN Plastin, CASP8 CASP9 CAT CAV1 CCDC85B (2.36) FGF7 (2.19, 3.31, Platamine, CCNE1 CCNG2 CD151 0D55 CDC40 2.06, 2.66) FMOD (2.38) Platiblastin, CDH3 CDKL5 CDKN1A CDKN2AIP GARS (2.10) GAS1 (2.17) Platidiam, CELSR2 CES2 CFHR1 CFLAR GCNT1 (2.06) GDF15 Platinex, Platinol, CIAPIN1 CIDEB CILP CKMT1B CLU (3.10) GPX3 (2.08) Platinol-AQ, CNTF CNTNAP2 COL11A2 COL4A5 GSTM1 (4.71) GSTM3 Platinoxan, CORO1C CREBBP CTAGE4 CTDSP1 (5.35, 5.57) GUCY1B3 Randa) CTH CTNNAL1 CYCS CYP2C9 (2.65) HHEX (2.27) CYP3A4 D6S2723E DAB2 DDIT3 DDR1 HPCAL1 (2.54) ICAM1 DENND4A DEPDC6 DIABLO DKK1 (5.72) ID4 (2.59) IGFBP3 DMBT1 DPYD DRAP1 DST EDN2 (2.97, 2.56) IGFBP5 (3.91, EDNRA EGFR ELMO2 EMP3 ENPP2 2.60, 2.94) IL13RA1 (2.10, EP300 ERBB2 ERCC1 ERCC2 ETV4 2.42) IL1R1 (2.41 2.24) F8A3 FADD FADS1 FAM129A 1L6 (2.67) ITGA9 (3.20, FAM13A1 FAM46A FANCC FANCG 4.54) KLK8 (4.87) LIMCH1 FAS FASLG FASN FEN1 FGF7 FGF9 (2.71, 2.26) LTBP1 (2.86) FGFR2 FGFR3 FKBP2 FMOD FOS MACF1 (3.18, 3.43) MAL FOSLI FOXC1 FTLL1 GADD45A (2.83) MEST (5.64) GALNT7 GARS GAS1 GCLC GCLM MMP15 (3.05) MMP16 GCNT1 GCNT2 GDF15 GLRX2 GMPPA (3.43) NEDD9 (3.66) GOLGA8A GOLGA8B GOLSYN GPAA1 PAFAH1B3 (2.04) PAX8 GPX3 GSTM1 GSTM2 GSTM3 GSTO1 (2.17) PFKFB4 (2.76) GSTP1 GSTT1 GUCY1B3 GUK1 PPP1R1B (5.31) PRKCI HERV-FRD HHEX HIF1A HLA-A HLA- (2.31, 2.39) PRODH DPA3 HLA-G HNRNPA1 HPCAL1 (3.77) PTGS2 (2.81) HSD17B8 HSPB1 HSPE1 HTRA2 PXDN (10.10, 19.71) ICAM1 ID4 IF130 IFITM1 IGFBP3 SCRN1 (2.76) SDC2 IGFBP5 IL13RA1 IL1B L1R1 IL4R IL6 (3.16) SERPINE2 (2.63) ITGA9 ITGAE ITGB4 ITPA JUN KCNA5 SLC39A7 (3.59) SPINT2 KCNK1 KLF2 KLK1 KLK10 KLK1 1 (2.47) SPON1 (2.80) KLK12 KLK15 KLK2 KLK3 KLK4 KLK5 STYX (2.12) SWAP70 KLK6 KLK7 KLK8 KLK9 KRT17 KRT19 (2.36) TGFB1 (2.40) KRT4 LANCL1 LAPTM4B LASS4 TRIB3 (8.75) TUBB2A LEPREL1 LIMCH1 LOH11CR2A LTBP1 (2.88) VEGFA (3.18, 355, M6PRBP1 MACFl MAD2L2 MAGED2 4.81, 3.82) WWC1 (2.43) MAL MAP2K6 MCM2 MED1 MED21 MEST MGMT MLL MMP10 MMP15 ABCB1 (-2.74) ABHD2 MMP16 MMP3 MPO MPP2 MRPS27 2.82, -2.33, -2.55) MT1A MT1F MT2A MT3 MVP MYC ADORA2B (-4.27) BIRC3 NAB1 NAIP NCOA3 NDUFS8 NEAT1 (-2.27) CASP3 (-2.56) NEDD9 NMI NMU NOTCH2 NOTCH4 CES2 (-2.08) CLU (4.24) NR112 NR4A1 NTHL1 NUDT1 OPTN COL4A5 (-3.24) DMBT1 P4HA2 PAFAH1B3 PAPPA2 PARD6A 34.95) DPYD (-2.07) FAS PARP1 PAX8 PBX1 PCNA PDIA3 (-2.69) FGFR2 (-4.25, PDXK PFDN5 PFKFB4 PFKP PGK1 4.25) FGFR3 (-2.10, PHIP PHLDA1 PLP2 PMAIP1 PMPCA 2.89) FOXC1 (-2.51, PMS2L11 POLA1 POP4 PPIE 3.13) GCLC (-2.03) GCLM PPP1R1B PPP3CB PRKCI PRKDC (-3.16, -2.98) GOLSYN PRNP PRODH PROS1 PSMB10 PTEN 3.36) GSTP1 (-2.04) HLA PTGS1 PTGS2 PTK2 PXDN QSOX1 A (-2.16, -2.37) HLA-G RAB40B RAD21 RALB RALBP1 2.84) KLK10 (-3.75, -5.35) RASSF1 RB1 RBCK1 RBM9 RBP1 KLK11 (-10.49) KLK3 RELA RHOC RING RNASET2 RNF34 9.01) KRT17 (-3.10) KRT4 RPL21 RPL36 RPL6 RPS14 RPS18 (-4.61) MGMT (-2.34) RPS4Y2 RPS5 RRAGD RUNX3 RXRB :MMP1O (-12.00) MT3 ( WO 2011/003911 PCT/EP2010/059648 27 SCRN1 SDC2 SERPINB4 SERPINE2 .8.76) NAB1 (-2.03) SFN SH2B3 SH3BGRL3 SLC15A1 NEAT1 (-2.29, -2.02) NMU SLC20A1 SLC29A1 SLC2A1 SLC31A1 (-7.07) PMAIP1 (-3.07) SLC39A7 SLC6A12 SLC7A1 1 SLC7A8 PSMB10 (-3.25) SLPI SNAP29 SNAPC3 SNRPA SOCS1 SERPINB4 (-71.70) SOCS2 SOD2 SOD3 SP1 SP100 SLC31A1 (-2.06) SLPI SP110 SPINT2 SPON1 SPTLC2 17.09, -28.09) SOD2 STEAP1 STYX SULT1A1 SULT1A2 2.54) TF (-15.98, -17.72) SWAP70 SYNJ2 TANK TERT TF TNFSF10 (-3.33) UGT1A6 TFAP2A TFB1M TGFA TGFB1 TLR2 (-8.88, -8.56) UPK1B TMSL6 TNF TNFAIP3 TNFSF10 28.60) VAV3 (-4.99, -3 12) TNFSF13 TP53 TP5313 TP73 TP11 WFDC2 (-20.20, -1505) TRADED TRAM1 TRAP1 TRIB3 TRIP13 TSPAN12 TUBA1A TUBB2A TXN DST (2.30,2.14,2 36, TXNDC13 TXNRD1 TYMP TYMS UCP2 2.86) UGDH UGT1A1 UGT1A3 UGT1A6 UMPS UPK1B USP14 VAPAVAV3 VEGFA VPS52 WDR46 WFDC2 WWC1 XIAP XPA XRCC1 XRCC5 XRCC6 YARS ZFP36L1 ZFP36L2 ZNF192 ZNRD1 PEMETREXED DHFR FAS FPGS GART GGH RBM17 (Alimta) 10 SLC19A1 TP53 TYMP TYMS (0+1) FAS (-2.69) 2.69 0.00 0 First of all, the scores associated with the drugs used in the first and second therapeutic lines are low (Gemcitabin = 129; cisplatine = 80 and Alimta = 0). Those scores are consistent with the observed clinical data. However, based on this score table, avastin associated with a score of 384 has been 5 selected. The treatment has begun in January 2010 and a major response has been observed after two treatment cycles, as showed with the scanners of Figure 5. This major response validates the present method and the unquestionable benefit for the patient. Example 4 The patient was 59 years old. He suffered of a non small cell bronchial carcinoma with 10 adrenal metastases. Two therapeutic lines have been used, namely three cycles of cisplatin Alimta and three cycles of taxotere-cisplatine-avastin. These therapeutic lines were associated with a first step of stabilization and then a step of disease progression. Normal and tumoral bronchial biopsies were carried out and used for mutational analysis by sequencing, CGH, microRNAs analysis and Genome expression analysis. 15 CGH profile is shown in Figure 6 and comprises chromosomal aberrations in chromosome 11. Mutational analysis including the genes as listed in Example 1 did not lead to the identification of any mutation. Based on Genome Expression analysis, scores have been calculated as detailed in 20 Example 1 and are shown in the following table only for some relevant drugs.

WO 2011/003911 PCT/EP2010/059648 28 '0 ::Found Targets List D Chemical Target Genes List (with fold-0 4):) changes) ECTEINASCIDIN CCNA2 (7.13) (Trabectedin ET- 4 CCNA2 CCNB1 CCNB2 E2F1 (40) N ) E2F1 5 13 5 13 512: 743, Yondelis) ( (4 85) GEMTUZUMAB 1 CD33 (1+0) CD33 (2 02, 2.63) 2 33 2 33 232 (Mylotarg)(10/) CCNA2 (7.13) CCNB1 (4.52) HYDROXYUREA CCNE1 (2.56) (Biosupressin, CC8(.0 (Biaurin' BAX C130RF34 CASP3 CCNA2 CCNB1 CDCA8 (4.40) CCND1 CCND2 CCND3 CCNE1 CCNG1 CDKN1A (2.35) Hydrea Hydre:a 26 CDC25C CDCA8 CDKN1A CKS2 EDN3 FAS (10+1) KPNA2 (2.00) 4 45 4 67 179 LHalurai Hydure' KPNA2 MECOM PRC1 PSRC1 RRM1 RRM2 (42 3%) (328) Litaler, Litalir. Onco- RU NATP3UECPSRC1 (2.71) Carbide, Oxyurea R RRM2 (14.29, 5.77) Ureaphil) UBE2C (7.67) BAX (-2.32) ABCC1 (2.14) ABCC2 (2.58, 3.19) BIRC5 (8.83) ABCB1 ABCC1 ABCC2 BAX BCL2 BCL2L1 BUB1B (6.66) BIRC5 BUBB CASP3 CCNB1 CFLAR CSF2 CCNB1 (4.52) CYP19A1 CYP1B1 CYP3A4 DPYD ERBB2 CYP1B 2.39) ILS DOCETAXEL F2R GSTP1 HIF1A HRAS IL6 MAD2L1 (2 9) (Taxotere) 4MAPK1 MAPK3 MDM2 PARP1 PGR POR (12+3) (2 68) PTGS2 (3.78, 3 56 3 87 107 PTGS2 RAF1 RB1 SKP2 TNF TNFRSF10B (34.9%) 277) SKP2 (2.18) TP53 TUBB TUBB1 TYMP TYMS UMPS TNFRSF10B (2.35) VEGF XTYMS (3 72) VEGEA XIAP BAX (-2 32) BCL2 ( 2.35) VEGFA( 2 35) 3 DHFR (2.02, 2.20, PEMETREXED DHFR FAS FPGS GART GGH RBM17 2 69 3 12) 1Alt (30 SL2.94 (2.59)8 (Alimta) 10 SLC19Al TP53 TYMP TYMS , :SLC19A1 (2.59) 2 94 TYMS (3.72) VINORELBINE C 92 CDKN2A (242, (Naveibine . 7 (2+0) :2.51) PTGS2 (3.78, i 2.87? 2.87 .81 :SLC29A1 TUEE2A Navelbine Base) (28.6%) 2.77) CISPLATIN A2M ABCB1 ABCC1 ABCC2 ABCC5 59 ABCC1 (2.14) (Abiplatin, 403 ABHD1 1 ABHD2 ABR ACSL3 ADAM10 (39+19+1) ABCC2 (2.58, 3.19) 4 23 5 04 48 Biocisplatinum, ADFP ADORA2B AHCYL1 AKAP12 AKR1B1 (14.6%) BIRC3 (4.02) BIRC5: WO 2011/003911 PCT/EP2010/059648 29 Briplatin, AKT.lAKT2 AKT3 ALB ANXA1 APEXi ARF6: (8.83) CA2.25)....... Carboquone, Cis Pt | ARHGEF6 ARID5B ARMCX2 ASS1 ASTN1 CCNE1 (2.56) II, Cismaplat, | ATP6V1G2 AXL B3GALT4 BACH1 BAX CDH3 (2.23) Cisplatine, Cisplatyl |BBC3 BCAM BCAS4 BCL2 BCL2L1 CDKN1A (2.35) Citoplationo, |BCL2L12 BIRC2 BIRC3 BIRC5 BMP7 BTG2 CREBBP (2.16) Lederplatin, |C1ORF68 C11ORF9 C19ORF2 C40RF29 CYP2C9 (2.09) Neoplatin, Plastin, |C70RF16 CA2 CALCB CASP2 CASP3 DDIT3 (2.09) DKK1 Platamine, |CASP8 CASP9 CAT CAV1 CCDC85B (14.15) ETV4 (2.22) Platiblastin, |CCNE1 CCNG2 CD151 CD55 CDC40 CDH3 FADS1 (2.84, 3.87) Platidiam, Platinex, |CDKL5 CDKN1A CDKN2AIP CELSR2 CES2 FEN1 (2.52, 2.70) Platinol, Platinol-AQ CFHR1 CFLAR CIAPIN1 CIDEB CILP FGFR3 (2.26, 3.09) Platinoxan, Randa) CKMT1B CLU CNTF CNTNAP2 COL11A2 ICAM1 (3.90) COL4A5 CORO1C CREBBP CTAGE4 IGFBP5 (2.04) IL1B CTDSP1 CTH CTNNAL1 CYCS CYP2C9 (4.60) IL6 (2.79) CYP3A4 D6S2723E DAB2 DDIT3 DDR1 KRT17 (3.17) DENND4A DEPDC6 DIABLO DKK1 DMBT1 MAP2K6 (3.28) DPYD DRAP1 DST EDN2 EDNRA EGFR MCM2 (3.84) MEST ELMO2 EMP3 ENPP2 EP300 ERBB2 (2.74) MMP10 ERCC1 ERCC2 ETV4 F8A3 FADD FADS1 (70.58) MYC (2.42) FAM129A FAM13A1 FAM46A FANCC PAFAH1B3 (2.10) FANCG FAS FASLG FASN FEN1 FGF7 PHLDA1 (2.20) FGF9 FGFR2 FGFR3 FKBP2 FMOD FOS PTGS2 (3.78, 2.77) FOSL1 FOXC1 FTLL1 GADD45A GALNT7 PXDN (2.31) GARS GAS1 GCLC GCLM GCNT1 GCNT2 SERPINB4 (2.63) GDF15 GLRX2 GMPPA GOLGA8A SFN (2.66) GOLGA8B GOLSYN GPAA1 GPX3 GSTM1 SLC7A11 (2.22) GSTM2 GSTM3 GSTO1 GSTP1 GSTT1 SOD2 (3.28) GUCY1B3 GUK1 HERV-FRD HHEX HIFA SPON1 (2.86) HLA-A HLA-DPA3 HLA-G HNRNPA1 TRIB3 (6.70) HPCAL1 HSD17B8 HSPB1 HSPE1 HTRA2 TXNRD1 (2.49) ICAM1 ID4 IF130 IFITM1 IGFBP3 IGFBP5 TYMS (3.72) IL13RA1 IL1B IL1R1 IL4R IL6 ITGA9 ITGAE UGT1A6 (2.26, ITGB4 ITPA JUN KCNA5 KCNK1 KLF2 2.11) KLK1 KLK10 KLK11 KLK12 KLK15 KLK2 KLK3 KLK4 KLK5 KLK6 KLK7 KLK8 KLK9 BAX (-2.32) BBC3 ( KRT17 KRT19 KRT4 LANCL1 LAPTM4B 2.27) BCAM (-2.07) LASS4 LEPRELl LIMCH1 LOH11CR2A BCL2 (-2.35) LTBP1 M6PRBP1 MACF1 MAD2L2 CASP8 (-3.17) MAGED2 MAL MAP2K6 MCM2 MED1 .CD55 (-4.47) CILP MED21 MEST MGMT MLL MMP10 MMP15 (-253) FOS (-2.24) MMP16 MMP3 MPO MPP2 MRPS27 MT1A KLK3 (-2.46) KRT4 MT1 F MT2A MT3 MVP MYC NAB1 NAIP (4.87) LEPREL1 ( NCOA3 NDUFS8 NEAT1 NEDD9 NMI NMU 2.15) LIMCH1 ( NOTCH2 NOTCH4 NR112 NR4A1 NTHL1 2.07) MLL (-2.34) NUDT1 OPTN P4HA2 PAFAH1B3 PAPPA2 PRNP (-2.27) PARD6A PARP1 PAX8 PBX1 PCNA PDIA3 PTGS1 (-2.24) SLPI PDXK PFDN5 PFKFB4 PFKP PGK1 PHIP (-2.68, -2.32) SOD3 PHLDA1 PLP2 PMAIP1 PMPCA PMS2L1 1(-2.54) SP100 ( POLAl POP4 PPIE PPP1R1B PPP3CB 2.51, -2.24) VEGFA PRKCI PRKDC PRNP PRODH PROS1 (-2.35) PSMB10 PTEN PTGS1 PTGS2 PTK2 PXDN QSOX1 RAB40B RAD21 RALB RALBP1 DST (-3.26, 3.55) RASSF1 RB1 RBCK1 RBM9 RBP1 RELA RHOC RING RNASET2 RNF34 RPL21 RPL36 RPL6 RPS14 RPS18 RPS4Y2 RPS5 RRAGD RUNX3 RXRB SCRN1 SDC2 SERPINB4 SERPINE2 SFN SH2B3 SH3BGRL3 SLC15A1 SLC20A1 SLC29A1 SLC2A1 SLC31A1 SLC39A7 SLC6A12 'SLC7A1 1 SLC7A8 SLPI SNAP29 SNAPC3 WO 2011/003911 PCT/EP2010/059648 30 SNRPASOCS1SOCS2 SOD2 SOD3 SP1 SP100 SP110 SPINT2 SPON1 SPTLC2 STEAP1 STYX SULT1A1 SULT1A2 SWAP70 SYNJ2 TANK TERT TF TFAP2A TFB1M TGFATGFB1 TLR2 TMSL6 TNF TNFAIP3 TNFSF10 TNFSF13 TP53 TP5313 TP73 TP11 TRADD TRAM1 TRAP1 TRIB3 TRIP13 TSPAN12 TUBA1A TUBB2A TXN TXNDC13 TXNRD1 TYMP TYMS UCP2 UGDH UGT1A1 UGT1A3 UGT1A6 UMPS UPK1B USP14 VAPA VAV3 VEGFA VPS52 WDR46 WFDC2 WWC1 XIAP XPA XRCC1 XRCC5 XRCC6 YARS ZFP36L1 ZFP36L2 ZNF192 ZNRD1 BEVACIZUMAB 1 VVEGFA (-2.35) 2 35 0 00 0 (Avastin) (0.% (100.0%) Accordingly, the following scores are associated with the drugs of the first and second therapeutic lines: cisplatine (48), Alimta (88), taxotere (107) and avastin (0). Those scores are consistent with the clinical data. However, other drugs are associated with better scores, for instance Trabectedin (512), 5 Gemtuzumab (232) and hydroxyuea (179). Example 5 The patient suffered of a rhabdomyo sarcoma with pulmonary metastases, proving that the present method is efficient to predict the therapeutic efficacy in any type of tumors. This is an evolutive metastastic disease derived from a fibromixoid sarcoma of a buttock muscle. 10 This intial tumor has been ressected by curative surgery in 2006. Subsequently, the patient developed pleural mesothelial metastases in 2007. After six cycles of treatment with a combination of Alimta and cisplatine, with a really poor response, the patient was subjected to a pleuralectomy. A novel pulmonary metastatic lesion was then detected with complex location prohibiting any surgery. 15 Normal muscle biopsy and pulmonary metastasis biopse were carried out and used for mutational analysis by sequencing, CGH and Genome expression analysis. CGH profile showed an important amplification of chromosome 16. It corresponds to an amplification of the PDGA locus. Mutational analysis including the genes as listed in Example 1 did not lead to the 20 identification of any mutation. Based on Genome Expression analysis, scores have been calculated as detailed in Example 1 and are shown in the following table only for some relevant drugs. Chemical Target Target Genes List Found Found Targets List (with fold- Avg Avg Score WO 2011/003911 PCT/EP2010/059648 31 : Genes: Targets changes) :Abs(FC) ,Abs(FC): UP-REG VINORELBINE CDKN2A PTGS2 1 (Nave/bine 6 RALBP1 RBM17 (1+0) :CDKN2A (48.68) 48.68 48.68 811 Nave/bine SLC29A1 TUBB2A (16.7%) Base) TEGAFUR :CDA CYP2A6 5 AD TP53 TYMS (1+0) TYMS (16.61) 16.61 16,61 332 (UFT) (200/a)53TY (20.0%)1 NILOTINIB ABL1 BCR KIT 2 BCR (5.91) PDGFRA (11.97, 10.80, Remarkably, Nilotinib is associated with a high score of 324 and is known to be active on the pathway of PDGFRA and PDGFRB. Accordingly, the inventors studied more precisely the PDGF pathway and obtained the following results. Primary seq. Sequence Description Fold Intensity 1 Intensity 2 Accession # name Change PDGFD Platelet derived growth factor D 18.6 114.8 2142.4 NM 025208 PDGFRA Platelet derived growth factor receptor, alpha 12.0 2072.6 24849.8 NM_006206 PDGFRA Platelet derived growth factor receptor, alpha 10.8 819.6 8964.0 AA559881 PDGFRA Platelet derived growth factor receptor, alpha 8.1 21.3 182.3 BC015186 PDGFA Platelet derived growth factor alpha 7.7 591.2 4576.5 NM_002607 PDGFRL Platelet derived growth factor receptor like 4.7 5397.2 25269.9 NM_006207 PDGFRB Platelet derived growth factor receptor beta 3.2 3327.0 10558.3 NM_002609 PDGFC Platelet derived growth factor C 1.1 23.1 23.7 NM_016205 PDGFC Platelet derived growth factor C -1.6 1028.7 615.7 NM_016205 PDGFB Platelet derived growth factor beta -3.7 375.2 101.8 NM_002608 A very significant activation can be observed. 5 CGH and Gene expression profiles corroborate to designate the PDGF pathway as an important driver of tumorogenesis for this lesion. Indeed, PDGF D is overexpressed 18 fold in tumor versus noram tissue and will activate receptor beta-beta. It is worthwhile to mention that PDGFRB is also overexpressed 3 fold. PDFGA is overexpressed 8 fold and receptor PDGFRA is overexpressed 10 fold. 10 Taken together, Nilotinib appears a good candidate of targeted therapies because it inhibits both receptors. The patient is awaiting for regulatory authorization for Nilotinib treatment. His attending physician acknowledged this therapeutic choice. In conclusion, the studied patients were all in therapeutic failure. For all of them, 15 there was no more therapeutic choice, and their their general status was prohibiting enter into clinical trials. Basen upon written consentment and upon request of oncology doctors, this method was applied. The present method allows the association of low scores for the drugs used in the previous therapeutic lines, illustrating the good correlation between low scores and therapeutic efficiency. Retrospectivelly, experts may envision that use of inefficient drugs WO 2011/003911 PCT/EP2010/059648 32 may have been avoided if such strategy would have been applied, saving time for the patient. It is important to note that the studied patients showed quite unique profiles, proving the potency of the concept for personalized medicine. Method can aply to any type of tymors as far as normal cells and tumoral cells of same hystologic type can be compared for a patient. 5 Another advantage is that the method can provide a solution with potential therapeutic benefit for all patients, whereas current methods based on compagnon tests can apply only for limited number of patients which harbour a given abnormality.

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CD C ) N t C) CD C)C: C) C) to C) CD C: to t CD C) ND CD C)D C C)C) C) C:) C)tU) C) CC ) C:)CA C ) 0C 0 0 C 0C )0 0C)) 0 C) mC) 0 C) 0C-) 0 C) 0C)C0 C) 2 2E 2E 2 2 2E C)C C' 2 C C' -2 2 2 2 wx x x xw w w w wx x x x x x x W ~ w O ] Of O ] U] Of U] O] U] O] U:: Of o] Of o] o] o] O] ofoUfC]c O fc-c OfQ - !Q fQ fo < Q ~ Q ~ Q 2 F-- <~Q 2 Q 2 F- - <F-<F-<F F - < -<F F -< -<F <F - -<F WO 2011/003911 PCTIEP2O1O/059648 173 C:) r- C:) L) C ) C1 c - C) o LO c C ) C ) C:) C:) C 3 C:) N- C:) C) C-4 !Z2 P- Ico (D C)0 C0 r- -- C) I.- co C.0 C.0 CO C0 C0 0o 'Nt C.) t r- rC) C:) r-) C:) - C)- U) -:t r- r- C U- r- LO CO CO LO CO LO NO U) N C0 C) C)4 C)i C- U) C) r- CO r I- N- N- N- C) C) N- I) - I) C - N - 1 <N rw CNC L D- c'i m~ m c co m o m /) >o m ID m co >U w ue < F- a-)0 1-~ .- 4 < 0 0 W LL LL LL a- a_ [I C: D D C U) Q) 0) C) a) a) C') 0 C 0M0 0~C~ U a- SD, ~ -&0>, -6 o) CL 2_ ~ 0C C/) CDaW C - C'J CJ 0n 02)15= =5 U) C ~ ~ ~ ~ ~ c C ]0oE0 TCTC-C) 0 C) C) . . 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a~~c col Cn ) E w~~~~1 _Y _ C*C C C )0 0 (N 10) C) coc C(4 1O 1.0 C) 1.0 = C) 10 66 -:1- "10 10 10 C) C:) CcCO CN r- m0 03 C) CD)( ', 03 1 (Nl CI I: 10 ( :?f 10 C - CO CD C) (Nl 00 10 t 10 4 t- 0; Cm 03 4 cn 10 103 10 (Z ) r- 10D I- 1 0 C, cy C) U) 103 'I 10 10 1.0 M0 10 LO 10 C) (N C) 10 _ 0 - C) I- 10 C) C)4 C)l C 14 C ! ) C O V- ) qCC I C. q (.0 C)j C) C) C) C) C) ) C) C CD CDI CD C)D C I~~LC I; I IDI i~ C) C: C) 'S0s C)I Z Z1 U] ~ ~ ~ ~ ~ ~ ~ : U] U] U] U ] U ] U ] U] U ]U ] U ] U w w U w w L U w w L U L L0 0 0 0 3 O c ) LU W M W W L x x xw w U L X X X WO 2011/003911 PCTIEP2O1O/059648 176 m) C0 C) 0) C)~C 00 C D I-ClC 7 ') - r- "- - -R L CD U) CD oC CD -t C0 CD C0 to -:t D U0) C0 't r- 0NJ 1- r- P-- U) r- r - D - 0NJ rD - LO (Ni U'3 C14 (NO U'3 - C D O ~ C D D C O ~ D C0 CDo ED co N- CD CDC - C C D C1O D N Cl) 2 2i 09 cc a- (I UO C ~ ~ ) Of 0J ~ ) co Of to co I - I < < ,< t< < < uj w U- C ai oi 0~0 a) Q) LL 0 50 76 (V C CD C C . ') 0 CV ( 0 _ 0 C) Cl I 0V Cl) Cu 2 ) 2) 2- E- 0V 0- 0ws c o 7 C 0 LL C 0 0 C -a L L Cl X C DL E E2 6- 0 (D) EE .> U) - C.) (V !;? OfW a) u' L 2> C EV cE 0N CU C 0 (I 0 Cl 00 Co L U ~ C 0 0~U E Q)0U) - 0 C 0 0) (CD 0 0 2) 2 ~ c LL Cl 0E -r3 ) 0 c- _0 Cl 0 C C 0) o E c 9 0 - C c" ~-0 V ClE a)5 a) (D C ) VOU 0 0 (dV- 0- -o C ) w )C uCu U) C l cu 21- -2 N- c C- 2)~ Cu' CV c CV~ ~~~ CV1 (V0)( 0 CV C cu' D C 5s C 0~- 0cu ~-~< C V('- > - -5 Cl~~u EC~ l l C 0Q C 0 C 0- ODQ 0- 0- 0 0- 0- C2f Of Of 2 of cr cu 2 C:) C:) CDl CO r- C0 C)3 m) CDO CD LO CA CDC) ) L)C) C3 - ~ 4 C~ C)C) C) S~C CA C) r 0) C C) C:) C:) C-4 C3 C:) C:) 'T CD II CDI I C:) C) CD a) CN- CD z~L zl 'o~z i ) C CD N' 2 L - CD -DC~ C) CD.- z~ ~ C) C) ) CD Cj CD C)l C) CD C)4 C\ C) C0'IC cq C) tC CD N- - C) CD (I C) C Z C N-) N-: C)-: C):4 (mi C)DJC)- C C) C), C) C) C) CD C) C) C) CNC D))) ) C ))) C CD C) C)) C ) C) C) C:) C) CD (NI C) C:) CY) C:) C) C)) C ) C) I I I I I I I 1 I IC) I LO I I I i( I: Cr C) CrC C C)U C Cr 0 r C Cr Cr C) CrC C)w Cr Cr C) Cr CrCr C r C Cr Cr Cr L Cr Cr Cr Cr Cr Cr) C WO 2011/003911 PCTIEP2O1O/059648 177 '0 L2 1L0 1o mO 1o (D, Lo Ci LO V) 10 1 1 0 0 1 co C\1 00 m) C\1 m~ (N (N ' C CO CD co CO) co CO - N-- CO C O \1 C C O C N\1 - 1= C- C)( 0 C) ( ~ C) - - - C ~ 10 C CO ( C14 CO CO) cOi CO cn) C O C O C O (D 1N- (D- CA- V-- CO i N- ) N- CO m-- N- N- C - - - LO 10- r- r- co (N - 10 - co (N - C. 1- CO - I-- C 0 0 -O (N COLPN N- O~ C/m :; - J J C C m o 8c ci Z- . 0 C, 0- < < m .D o -- < < ~ ~ c U- U-<1fQ U- c i _j O C:)C) Co) <oCC 2-1 oq 0CO : 2F - a _ a_ a _ [f ) > 0~ 0 0 0l. (D 00) C 0 75 0 Cc) 0 0) L D C~~) 02 CD ,- ~ 0' (N 0- 0 a) NC) CO W' 1 2: 2 0 0 C~~~~~fU (D 0 C )0 0 > < ou ao cu 2 2 2 o co .5 r- :2 22 2 CC) r,- 0 ~ CY 0 a, ~ ~ ~ ~ w L, A, Eoo - ~ - ~ - , - , ~ ' CO2 O ~ 2 2 2 2 2 2 2 0 ' E ' E CE) 0 0 0 0 0 0 0 O) CO UO co 52) (n CO C: CO O ) 2 O Co CO Co Co c :z CO) C ' ) C DI) o CD CO) O CO L O CO 't CO4 C) CC3 C (=1 CD CCO CO CO) CO I C') m' !'- I I CO CI CO) CO CID CO0 r- LO CD 00m L DL O r CD m r- CZ 00 P- CO . CID P C) 0- a - - C N- OLN-OO C O CO CD CO 10 CO L O 00 C') N- - CO CO C CO CO CO CO C) C O C O O C N C CO C CO1 CO CO COCIO C O C CO C CO CO C' CO CO CO) (N CO C) CO C) CO CO CO C) CO C) CO CO C CO CO C = I IN 2E I 2E I 2E 2E I 2E 2E = 7 = = w w x wg g g w w wg Lc, uZ ~,LcoLD B Ow w w Ofo I < ~c ~c WO 2011/003911 PCTIEP2O1O/059648 178 00 CD CN

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Coo co r- I- 0 CD 03 CDj 0 C6 0 0 X C) 00 m )C) C0 1.0 (0 r cc C0 00ll- 00 0 0. C1 0) CZ) C: ~ ~ ) 0 3 030 03 - P O L (0 C.- m F o (0 t- t-(O0 0r .:t m'. CD 't0 030 -I F- - F-l U) m3 CD C0t3D C (0! I0 F3 0 -03 032 03 03 03I (Nj F031!! ! 0 3U" 0 3 a - 3 3 3 0 3 3 - F 30 3 0 CD ~ ~ ~ t 0- E~C) < F ~0 ED a) 0 0C) Ei C E- 0L0 C) C)u 0 CD C2f- E I- )- : 0U 0 C2 LL 03 0 :CL E C >1 = - CL0 U) a) cu ) cn2 U) a D- E U) N CD 0 ) CM - 0 2> E-0 LL 5- >- 0 a) C x o E5 CY- >1) 0- 0 E 0 2E < 2:1' 0 F-2 2C EE Ci U 2U O D L) C.0 E E~L 0 0 W 0 U a u -r a) 0 - 0 Q)'( 02 U) -cu (n 0. c 0 -p ol a) co7 oc uQ O C 0~~ 0 EL- ). -0 S 0~ C 0 0 U) cc m - 0 0(D CL U))0 0 s0 2 0a)O~0 0 - ~ 2 0 2~ OC :3 - ~ ) 03) 03 05E 2 5 ) co.= )0a - 1: (D C C a U 0 9 -j -9 -0 . - 0 o- n) w ~~ a - E~ a - Eo c)(1 E- >,WU) E ~ C - U) 0RI0< &E < Ep3 C m 0 03 CDUD ~ C:), C ) LO( I: N l cc::)) 3 00 1- 02:5 ,3 - ( CD3~: CD L cn 3 :o( (0 C.0 F m : P,, C 'T C)0 C) 03 -C Li5 E c:) I2E2i 0 2i CI =-00 = 2i C:) m3 Y3 :) Lo 1)m . LO2: ~ 0 LO3 2 m0 F -: (Ni C) C330 03= 030 0 03 - - 0 =0FD33C 0 0 ' -- 30t30 2 CD Lo0 2 0 3 -3 03-O 0 13 0303 C C) 030 033D . 03 C3 I03N.~0 0 CC) I~~V IMi I~ I I II I;F F I I m mU mU mm m m mLuJuJ w w 0 0 0 00 0 00 00 55 0) C) C) 0)C0 C) C) C) (- C :i W W LU w w w ww L w w C-) C) C) C) C-) C) w w Luj w w] WW UW W 0 0 0 0 0 0 C-) C) C) C) C) C) C) C) C) C) C) C) C) C) C-) C) WO 2011/003911 PCTIEP2O1O/059648 188 (D ) a) 0 t 0 a 0~~~> 0- 0- co C14 C)4 co co co co co N- I-- to - -,1 N- N N - z; C tor co to to !NL 'oo~ o C' C ) C' C') tO C') C) c') c') c') 0C C 11~st m -z 0- (D r- L o t oo -OC Cs-i~~~~L LO ClrC- -N o ~ lC - to C. N-t l C4 Cl C4N C)4 N-- 00 m' N-- N-0 ml C)t t o 00 Cl C Cli N- to- N- rto to to to0 co tot o m co m) <) _o m ~c' Ne Nl > - m )as < c'a m a a_~t w L LL Ow C3 < U- - t m c 0o to (D cu E E) 0 o) a a) C ) 0D E ED 0 E C CLC 00Cd m ) () ELZ w ~ a) C) 6 0- 0- 3ocC' _u cu (D 0 -Fo 0- M- 0- Ec .2 U) -cn)a F- - 7 cu C> -2 :, 2 C4 U0 _' cm (n CD u U)~- a) 0 U)a) C) W) M) 2 C 0-_ 0- - cu E--, U C- 0- c- aD z > C) -0 m w m . F 9- P U CU .2 0~a a) >C 0- M - E EU) E 0 CD CD) U)00cu 21 -a a) - ~ e-~ ) a) a) 0 CE C- t) 0 ) a) a) a) a').- .- 0 0 Cn CL U 2 0 cm C) M~~) cu U) 0 O -a0U 0 -C) .f :t_ (D L)CO CO -i 2~ 2 E .. E E iiC P2 o E ~ ~ . ~ C') CC) N-CAC C) =- CC) C), C) IO ICD' C O' D LOC N-- ) r- m U - C) too C to C) -I- -;I C) C)to r-- CN CO a) C) C) C) C C)- C:) CA tC.0 ) C) ID I toto C') IZ Cto C Cl) C) 2: C)C N-00C C)C) m r Cj CA CD 1 C C~C) Cl LO C'~ C) ) Cl N- ) C) o LO tor- - to0 C-) -q 00 Z Cl N- m) to IO jt - NC) (.0c') N-N Cl C- C) CY) C) 5~ ~ C) C)C'~ C~ C)- 't C) CD C) TC2CD CD C) Cl C)NP- C.,::: ) C) (,:) C) C)' C')Cp C)C) CC) C) C) C) C C, C, C) SK C) C)) C ) C) . C )) C)C) IiS~~ co 1' 0io1- :5 I ItC Ot IIl II) 'I' w w iU w w i Lu w w w iu w w w w 22 5 5 5 5 55 5 5C)C) ( o C) C) C) C)) C ) C) C) C) C-) C) C) C-) C) C-)< < -] -i I j -i -i -i ] j -] - -i -J -j -i -i ] - - (D 0D 0 0 0D 0 0 0 0 0 0 0 0 0 0 010 moc mmwwu L C-) C) C) C) C) C) C) C) C) C) C) U) C) UCIC) 0 00F - WO 2011/003911 PCTIEP2O1O/059648 189 CDw CO CO a)( 4::O 0) co 0)0 cu -0 -0 -p cu cu co -0 ((oC 00 0C 0- - 0-U 00 0 0co 0 0) a0 ) ) a) a) T) 0 0 000) 00 OD 0 I-- 0)- 0) : r oo 10 coCC 0 C14 M D C5L 0 0) 0 1o0 00CN 7t 7t- m- 00~-N 1o m I 1003C 0')0 CO N- 03 0 co 100 10 1 -LO ~ 1 0 1N 0)1 10 10) 10 L 03 0303 C03 U)0) CYL0) ) m)1003m0)1200 m)1 m !;2 -_ r- r-_ r-_ 10) U10 0 ) Z; -- N- m=0 -,-- ) N - - 0) :) 0:) C0) 10 101 101 NOL - m) N- 10t- N- 0) I)1 --11 -1 -N C'i4 N C NJ C-4 CA r- l-- m) 0))-0 C10N )0) - co0 0)0)n 00N N (J N 3 00 00 00) 00 0 0 0) 100 00 0 0)- 000 10- - - - N N-N- 030 0 s r3-3 .0 ~030 - 03- 00 m <0 < < -i-- mN0 10 N~ c - 03 m) Mm 0) 0) C- U- cj-i cjC) C- a_ a- a o- a >- C) a- a_ I- __- JCC/) U) C/) o c) ao >- >- 0 e < ~ CQ D 0 Of Of < < < < <C 0 ) C) o - m m ca mm r mm mO MDC C) ca co a) 0 a ~ Q )) a) (D 0) a) U) 0) W (0o W0(0 ( 0)0 00 CDO a CD a) M - <f CL c~-~ - - ca E a) s s)0 S; s) 0- C4 C) 0O a) 0 ) ) a) 0 _)2- 0 _ 0 0, 0 o0 ) -c c u - 0 - 0 E ) c u S0 S j 0 1 0 *> 31 5- ::: 0 ) 0 ) 0L wco CU (n U)( (D _0 - 0 - _0 u ) ) a) a) a) 2 . 5 . 0)5 A- (D 005 5 uo =0 ID 0 _ 0 0 0 0 00 ) 0 1 -55 E E 0r i6 i62 C- (0 C;-'-) C; ro C E O0 m 1 0- () 0 (0 0 _0< E-FD E~ CD a) 0 5 0 X. 0- 0 .- .CD 0D E -o0 0 0 0 C-L CO - 70 < CL CL~ CL CL C C U L OL D u0 ) a - a - 0- 0 0 0 0 0~C~ Z O CC CO C'0- C- 0 0) Q) 2~ cm0 0) J 5 E 8 8 2 -- ' - 0 (0( (0 E5 2 0 w (D mm mm mm ml .) 0l 0 10 0) 10 -0 -0=0 )U ) U10 0N 0) 0) 0) C-0 C 0) -L E L) a_ c.=0 U c i i a) mN C) 0)0) C r - 10CDCl 00 (0 0 00 ) 65 0 0) N 100) 1010 0) 0 0) )0) ) 0)- 0 0)0) C) 0 00 ) co 0 7tz 0 Lo CC CD- Z Z

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N I 0)N 0) 1o 0)Z r- C.0~ t 0 0 1010 CNJ 0N0 m - 0) 0)10= '0D - N- -- - - - - - - - - - - - - - - - ) 0)0m (NJ ) (D C6 <M CC))0)C N- 0) OZ) r-) - 0 - - -- - - - - -0 "3(NJ C:) C) C0) col 0) I I I 10 I I~ I I I I' I I ID 0 mD m - C:)-- c-0 CZ)- o F- I- O F- i F-F F-: F- F-F- . C4~ Q0 r- C/) -4C/ COD/ C'CT C) coC/ CC) 27C/ C) C/ 4 CO) c0000 0 00 0- 00 0 0 0 0 D 0 0D0 00 1 0) 05 00) 0~ 0 0 WO 2011/003911 PCTIEP2O1O/059648 190 c 0 0 u -0 -0 cu 0 -0 - 0 0

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0 t50 0 0) UO) ) a aa 00- 0~~~ a) (D0 2 00 a) Z) Za))Q)2c: a)i 0 5wwmm:t) 0:) m to - ;t 00 00 0o to to 0: 00 C, m U)U) Ua3 aO aO m a) 'ta)- CO 0 N- N- N- N -- N- .- ; ; m C 0 !t- r .- 0 ) N l r- r- Ia) t- -- m c -m o D00c - I - r_ i 2 Cv) -t CDC~ C:) C:~ ) C~ (co co) m ~ (co m co((- 0) co' - o m)' m0 CD( 00 00 c 00 0) 0) 0)0 ' (N 0 ) 0 N N o o N 0 mo C.0 o N- (C cN 0N C ( N N j ~ a Qaa ca D- D-< F C aC L o! E < -- - Q Q O{ - << < < U- < a w 2:1 ED O~ 0 C1N -N < 0 a)0 Z) 0_ _ 0 < ~ c (3) > Z5 C-1 -11 CU 0 ) a) a) w (D U) -0 -0 0- 00 0 U) / -- a- U) ZD E Ec c C3 c u CL0 D C 0 _ E U) (n a) a0 E EC.0.O(a a) 00 0D 0D D Z5 -- co 2' 2-~ 2a a 2 -C, C-1 2(D a (o 0 - 0 E < ~ - ! u) u) u)' u)- cc - - ) U) Ua C a) a) aD C_ (D '5 y w c c 0 2 c N 0 0 0 0 00 0 ( 5- D5 0 0 0 a) a) Q) Q) 0- 0 ) oo o EE E Ea)c4u cc c cu co~( 0 (01 ' a) (D 0) ( 7(C00 a) ( a) a) to' 0) ( a) 0 a) a) a) a) a) a o) o0 I to E)a E) Dao a) E- cI2 0 a)) 0) 0) ) a) C)0 ) 0 ) 0)0 0 )0)0 ) 0 ' ) N- CD CD 0 I C I I I I I ;K I I I (N ; m- F; - -F-F- F- F-FL - F-F---- F(0c -- F-F- V - F-, - F- 0 oo a.0 a)4 'IT ) aZ) 00a CD .0 al oazaODa) 'IT U) 'CID a) -1 a) ) a)1 oc o 0 00 0' 00 000 0 00 00 0 0' 0 0 0 0 0 0 0 00 0 0 0 0 0 00 00 0 0 WO 2011/003911 PCTIEP2O1O/059648 191 co :- : > - , cu 0 ~o o-o 0 0 0 0 a)a ( (0 N- >c -4aC) 0) .0C 0- U ) )L (.0 a) (N 0 0( -1 t- CD m3( CA OD 03 C5 C-5 c (C-; C,5 00 U. mco) r.-I-- N-Nr- N- cn - N N-N1- coNr- N.- co (0 N-am M M' (C C) m3 N-a C> c m~a m 3) m ) N -- m) SO00 00 303 03 c 'l 03 C-4C~ CO.0 r-C - C'4 '. (0 C/) Q li U-- -J L C)< < m LU 2:) 0 -a 0 ~E E E 0 C (D E> a) E 0 Q) 0) 0 )7 0 -~ U) cu > UL 0 cu 0 CD 0 D 00- 05 C a) 0 0~ Q_ 00 _0 u4 E (DlC 'F 00 0 -2 2 Q - 7 Z e) ia a) LO I-2 C:) a) 0) 03 LO N 03l C) CN C) a ) I I I C I 2 ! 2f : 2:: 2 CO U) () U CO) U)U) Cl) 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 WO 2011/003911 PCT/EP2010/059648 192 Table 2 C B1ACC0BC2AC1 77)LBC ( L9 MEHTATE AI)D D8C8 (3 75) NAT1 (2~ 39)ARP Lcc LL.

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Nucleotides Nucleotides > Antimetabolites Nucleotides >> Antimetabolites >> Folic acid ABCBi ABOCl ABC2 ABCC 1 (7.70) ABOO2 (4.39, ABCO4 ABCG2 ALB ALPI 5.45) ABCC4 (2.34) ATIC METHOTREXATE :ATIC 004 0068 CD8A (3.75) NATi (2.39) PARP1 (Abitrexate, Antifolan, CYP3A4 DHFR DKK1 (2.68) PRC1 (3.44) TYMS Arbitrexate. Emtexate, GSTM1 ICAM1 IL8 MMP3 19 (3.69) UBE2C (9.47) WNT5A Folex, Ledertrexate, 35 MTHFR MTR NAT1 ODC1 (10+9) (28.92) 7.36 6 93 197 Metatrexan, Methotrate, PARP1 PRC1 RB1 SELE (54.3%) ABCB1 (-3.59) CD68 (-3.38) Mexate, Rheumatrex, SLC19A1 SLC46A1 CYP3A4 (-7.84) DKK1 ( Trexall) TAGLN TIMP1 TYMS 16.44) ICAM1 (.8.24) ILB ( UBE2C VDR WISP1 16.97) SLC19A1 (-3.24) WNT5A TIMP1 (-4.76) VDR (-6.07) PEMETREXED DHFR FPGS GART GGH 4 GART (2.18) TYMS (3.69) 8 RBM17 SLC19A1 TYMP (2+2) SLC19A1 (-3 24) TYMP (- 3.50 2.94 73 TYMS (50.0%) 3.83, -5.95) Nucleotides >> Antimetabolites >> Purine ADA AKT1 ARNT BCL2 CCNA2 CCND1 CCNE1 CDKN1B CYCS DIABLO CCNA2 (5.27) CCNE1 (5.62) FLUDARABINE ERK HIF1A HIST3H3 6 HIST3H3 (2.04) VEGFA 25 HIST4H4 MAP2K1 (4+2) (2.60) 3.56 3.88 62 (Fludara, Fludura) MAP2K2 MAPK1 MAPK3 (24.0%) MCL1 PDCD8 POLA1 CCND1 (-2.52) MCL1 (-3.34) RRM1 STAT1 VEGFA XIAP Nucleotides >> Antimetabolites >> Pyrimidine ABCB1 ABCC1 ABCG2 ABCC1 (7.70) ADSS (3.13, ACADL ADSS AIFM1 3.02) BECN1 (2.18) CA12 AMER AMT ANGPTL2 (21.62, 17.07, 11.24, 25.25) FLUOROURACIL AREG ATP50 BARX2 CASP2 (2.27) CASP3 (2 14) r BAX BBS4 BCL2 BDH1 CASP8 (8.85, 2.67) CCNE2 Adru5l FUlrrac, BECN1 BIRC5 BNIP3 (3.25) CCNF (2.52) CDCA8 Arc/, ruem, BNIP3L BOLL BTG3 BUB3 (2.55) CTNND2 (3.12, 2.44) Efud EfudEu , C130RF34 CA12 CASP2 .CTTN (2.13) DTYMK (2.69, U, Fluoroblastin, 13 CASP3 CASP7 CASP8 64 2.60) E2F3 (2.71) EML2 CASP9 CCND3 CCNE2 (35+29) (4.55) FEN1 (2.79, 2.41) 7.06 5 18 137 Fluorop/ex, Fluraci, 2CCNF CCNG2 CDCA8 (48.5%) FOXO3 (2.21) GOLGA8A F/uracurum, Ft~ Furuai CDKN1A CDKN1C CIDEB (8.56) GSTP1 (3.00) KPNA2 ' CKS2 CTNND2 CTTN (2.49) LTB4DH (18.28) Kecimeton, Phthoruracil' CYR61 DPYD DRD5 MED13L (6.09) MELK (6.04) Phtorurac, Querop/ex, DTYMK E2F3 EGFR MKl67 (5.91) NDUFA10 Timazin, UR, U/up) EIF2B2 EIF3S3 EML2 (2.42) NKAIN1 (22.33) ERBB2 ERBB4 ERCC1 PARP1 (2.68) PCDHA5 ERCC2 ERP29 F3 F8 (2.75) PRC1 (3.44) RAD23B FANCG FAS FEN1 FGF7 (2.57, 2.55, 4.15) RNF34 WO 2011/003911 PCT/EP2010/059648 193 FOXO3 GADD45A GAMT (2.18, 2.49, 2.45) RRM2 GFRA1 GOLGA8A GSTP1 (3.30, 3.47) TYMS (3.69) GSTT1 HINT1 HIST1H1D UBE2C (9.47) ZNF552 (8.76, HNRPC IGFBP4 IL8RA 2.45) IRAK1 JMJD2B KIAA1467 ABCB1 (-3.59) ACADL ( KIF3A KPNA2 KRAS 19.18) AMFR (-5.37) LGALS8 LMNB1 LTB4DH ANGPTL2 (-2.89, -3.89) M6PR MALT1 MAPK13 AREG (-119.30) BTG3 ( MAPT MED13L MELK 7.01) CDKN1A (.7.85, -4,44) METRN MK167 MYC CDKN1C (-7.69) CYR61 ( NDUFA10 NKAIN1 3.69, -2.81, -3.06) DPYD ( NUCKS1 PARP1 PCDHA5 2.82, -4.07, -4.30) EGFR ( PDAP1 PGR PPP3CA 12.64) ERBB4 (-3.94, -3.33) PRC1 PRNP PSCD3 F3 (-3.63) F8 (-3.51, -2.90) PSG9 PSRC1 RAD23B FAS (-5.93) FGF7 (-2.01, RAMP1 RNF34 RPL3 3.09, -3.61) GAMT (-3.39, RRM2 RTKN SCUBE2 2.43) GFRA1 (-5.66) IGFBP4 SERPINE2 SFN SNAPC1 (-2.66) IL8RA (-5.26) TERF2 THNSL2 TMSB4X LGALS8 (-2.28) PGR (-11.29) TMSL8 TNFRSF10B TP53 PPP3CA (-2.42) PRNP ( TYMP TYMS UBE2C . 3.95) TMSB4X (-2.05) UPP1 UPRT UXT TMSL8 (-11.70) TNFRSF10B WBSCR1 XAF1 ZNF32 (-2.25) TYMP (-3.83, -5.95) ZNF552 ZNF582 ZRSR2 UPP1 (-3.24, -2.71, -2.28) CAPECITABINE 5CES1 CES2 CES3 DPYD 4 CE (13.90) CES3 (10.19) 5 (3+i1) :TYMS (3.69) 7.88 9.26 555 (Xeoda) TYMS (80.0%) DPYD (-2.82, -4 07, -4 30) GEMCITABINE (DDFC, DFDC, GEO, Gemcin, Gemcitabina, BAX BCL2 CASP3 CASP3 (2.14) PARP1 (2.68) Gemcitabine, HC1 CDKN1A CMPK PARP1 (3+1) TYMS (3.69) 3.66 2 84 70 12 (+)36 .4 7 Gemcitabine PDPK1 RBM17 RRM1 hydrochloride, SLC29A1 TP53 TYMS (33.3%) CDKN1A (-7.85, -4.44) Gemcitabinum, Gemtro, Gemzar) DNA DNA >> Alkylating agents DNA >> Alkylating agents >> Nitrogen mustards CYCLOPHOSPHAMID ABCB1 ABCC1 ABCG2 ABCC1 (7.70) BECN1 (2.18) E (ASTA, Asta B 518, Cp, AMFR BAG1 BBS4 BCL2 CA12 (21.62, 17.07,11.24, CA BECN1 BTG3 BUB3 CA12 25.25) CASP3 (2.14) CTX, CY, C/afen, CASP3 CASP9 CDKN1B CTNND2 (3.12, 2.44) Claphene' CTNNBIP1 CTNND2 CYP2B6 (10.92, 3.17) E2F3 Cyclophosphamid, Cyclophosphamide CYP2B6 E2F3 EGFR (2.71) GSTA1 (16.89) ILF3 Mopohrame, EIF1AX EIF4EBP1 ERBB2 (3.78) MED13L (6.09) MELK Monohadre ERBB4 ESR1 ESR2 (6.04) MK167 (5.91) NKAIN1 oSterie GAMT GFRA1 GSTA1 29 (22.33) RRM2 (3.30, 3.47) ' 56 GTF3C1 IGFBP4 ILF3 (17+12) ST14 (2.22) STK39 (6.44, 6.89 7 19 218 Cyclophosphamidum' IRS1 JMJD2B KIAA1467 (51.8%) 6.78) ZNF552 (8.76, 2.45) Cyclophosph an, KIF3A MAPK14 MAPT Cyclophosphane' MED13L MELK METRN ABCB1 (-3.59) AMFR (-5.37) Cyclophosphoramide' MGMT MK167 NAIP BTG3 (-7.01) CTNNBIP1 ( Cycfsfind NKAIN1 PGR PLOD1 7.17) EGFR (-12.64) ERBB4 yklo m, PLOD3 RAMP1 RRM2 (-3.94, -3.33) GAMT (-3.39, Cytophosphan' SCUBE2 SRM ST14 2.43) GFRA1 (-5.66) IGFBP4 Cytoxan, Cytoxan' STK39 THNSL2 TP53 (-2.66) IRS1 (-13.62) PGR ( Lyoph, Endxa~noxnR, ZNF552 11.29) PLODi (-2.05) WO 2011/003911 PCT/EP2010/059648 194 Endoxan-Asta, Endoxana, Endoxanal, Endoxane. Enduxan, Genoxal, Hexadrin Lyophilized, Cytoxan Mitoxan, Neosar, Procytox, Rcra, Waste, Number, U058, Revimmune, Semdoxan, Sendoxan, Senduxan, Zyklophosphamid) ...................... ... O.......................................................... .................. ........................... ............... (Cyfos Holoxan, 1000, BCL2 CASP9 CYP2A6 CYP2B6 12 3.1 ) IFEX, Ifex/Mesnex Kit, 1CYP2B6 CYP3A4 CYP3A5 5 11T00.4 08I122 10 (+):1.040 2 lfosfamide/Mesna Kit, DNMT1 GSTM1 GSTP1 50+0) 11.007848Y322 Isoedoxa, Mtoxaa, GTT1(50.0%) CYP3A4 (-7.84) CYP3A5 ( /soendoxan, Mitoxana, GSTT1 3.4 Naxamide) MELPHALAN (Alkeran L-PAM. L Phenylalanine mustard L-Sarcolysin, L Sarcolysine, L- ABCC1 BIRC5 CASP2 5 ABCC1 (7.70) CASP2 (2.27) Sarkolysin, Levofalan, 9 CASP3 CDA GSTA1 (5+0) CASP3 (2.14) GSTA1 (16.89) 6.40 6.40 355 Melfalan, Mephalan, GSTP1 MGMT MGST2 (55.6%) GSTP1 (3.00) Phenylalanine mustard, Phenylalanine nitrogen mustard, Sarcolysine, Sarkolysin) DNA >> Alkylating agents >> Nitrosoureas ABCB1 ABCC1 ACTC1 ABCC1 (7.70) AGT (3.57) ACTG2 ACTN1 ADAM15 AKT2 (2.40, 3.07, 2.01) ADAMTS4 ADCYAP1 ALAD (4.21) AMH (2.39) ADRA1 B AGT AGTRL1 AQP8 (3.03) B4GALT5 (2.55, AKT2 ALAD ALOX15B 2.84) BAIAP3 (2.18) BECN1 AMH APC2 AQP8 (2.18) BICD1 (13.67) BLK ARGBP2 ARPC2 ARTN (3.63) CDC25C (8.00) CDK8 ASMTL ATP1A3 ATP4B (2.39) CEACAM3 (2.96,2.75) ATP5D ATP6VOC ATXN2L CHEK1 (4.69) CKM (20.86) AVPR1A B4GALT5 CLTCL1 (5.12) CRABP1 BAIAP2 BAIAP3 BCL10 (3.09) CYP19A1 (2.64) CARMUSTINE BCL2 BCL2L1 BECN1 DHX16 (2.71) DVL1 (2.75, BICD1 BLK BMP7 BRF1 2.21) EIF2B1 (2.16) EPHX1 BCnCaubras' 23 CA6 CALB2 CCL2 CD151 5 6) EZH2 (4.80) FNTA 5 24 4 73 102 Gliadel Gliadel Wafer 0 CD68 CD79A CD80 1 (3.94) FTH1 (242, 2.32, 000250l 00K8de WaKerA (46.1%) (.4 Ti(.2 .2 2.35) FTL (3.51, 4.41,3.0 ' CDC25C CDK8 CDKN1A 25FL31413.40) Nitrumon) CDX4 CEACAM3 CEBPB GCM1 (2.70) GOLGA2 (2.44, CHEK1 CHRM4 CHRNB3 2.57) GPR37 (2.96) GRB7 CHRNG CKM CLTCL1 (2.78) GSR (10.75, 10.84) CNTNAP1 COL9A1 GSTA1 (16.89) GSTP1 (3.00) COX6A2 CRABP1 CRAT HNF1A (3.45) INPPL1 (2.26) CRYBB1 CST5 CTF1 KCNH2 (4.05) MAP4K2 CTNNAL1 CYP19A1 (2.72) MAPK10 (3.15) DHX16 DNASE1L2 DVL1 MAT1A (6.12) MDK (17.02) ECM2 EEF1G EIF2B1 MED1 (2.44) NPAS1 (4.81) EIF6 EPHX1 ESRRA PRC1 (3.44) PTPN3 (2.46, EZH2 FBN1 FKBP1A 2.03) RAD51 (4.60) FNTA FOXJ1 FTH1 FTL SLCO1A2 (2.31) SMPD2 WO 2011/003911 PCT/EP2010/059648 195 FYN GABRA2 GABRA6 (2.55) SNCG (2.23) UGT8 GAMT GAST GCM1 GD11 (5.58, 9.40) GD12 GFAP GHSR ABCB1 (-3.59) ACTG2 ( GNRHR GOLGA2 9.33) ACTN1 (-2.46, -2.62) GOLGA4 GP1BB GPR25 ADAMT S4 (-3.13, -5.12) GPR32 GPR37 GPR65 ADRA1B (-7.78) ALOX15B ( GRB7 GRN GSR GSTA1 17.05) ATP1A3 (-5.85) GSTM1 GSTM3 GSTP1 BAIAP2 (-4.34, -2.72, -3.14) GTF2F1 H2AFX HADHSC BCL10 (-2.16, -3.00) BMP7 ( HAGH HCFC1 HMGN2 16.37, -4.33) BRF1 (-3.38) HNF1A HOXA4 ICAM5 CALB2 (-5.76) CCL2 (-8.86) IDS IER3 IKBKG INPPL1 CD151 (-2.41) CD68 (-3.38) INSIG1 INSL3 IRS1 ISLR CDKN1A (-7.85, -4.44) ITGA3 ITIH1 KCNH2 COX6A2 (-2.12) CRAT ( KIF5A KRT17 KRT31 3.15) CST5 (-2.40) CTNNAL1 LAD1 LAIR1 LAMB3 (-5.85) FBN1 (-2.95) FKBP1A LMX1B LOXL1 LRCH4 (-5.05) FOXJ1 (-5.38, -2.05) LRMP MAD1L1 MAP4K2 FYN (-2.59) GAMT (-3.39, MAPK10 MAT1A MDK 2.43) GD12 (-2.36) GPR65 ( MED1 MGAT3 MGMT 2.59) GSTM3 (-3.01) HOXA4 MSR1 MUC2 MVD (-8.73) IER3 (-4.31) IRS1 ( MYBPH MYD88 MYOD1 13.62) KRT17 (-5.21) LAIR1 MYOG NDUFC1 NKX2-2 (-2.82) LAMB3 (-23.74) NPAS1 NPBWR2 NRTN LOXL1 (-2.57) MAD1L1 ( OMP PAEP PAX8 PBX2 2.73) MGAT3 (-12.97) MSR1 PCBD1 PCDHGC3 .(-2.93, -2.73) MYBPH (-4.47) PCOLCE PCYT1A MYD88 (-2.51) NRTN (-7.31) PFKFB2 PFKM PFN1 PAX8 (-5.65) PCOLCE ( PIK3R3 PMS2L11 3.31) PZP (-2.82) RCN1 ( POLR2E PPM1G PRC1 2.52, -2.24) RNASE4 (-2.13) PRL PRM2 PSCD1 PTPN3 SGCA (-17.29) SLC16A2 ( PZP RAD23A RAD51 6.80) SNRPB2 (-2.25) RAD9 RCN1 RGR RIT2 SOCS1 (-5.85, -5.20) SPEG RNASE4 SEPT5 SFPQ (-5.28) ST8SIA1 (-6.37) SGCA SHMT2 SHOX TCF21 (-20.30) TSPAN4 ( SIAT1 SLC1OA1 SLC16A2 3.44, -3.61) WIPF1 (-2.18, SLC30A3 SLC6A2 3.58) ZYX (-3.12) SLCO1A2 SMARCD1 SMPD2 SNAPC1 SNAPC3 SNCG SNRPB2 SOCS1 SPEG SPRR2B SRP14 SRY ST8SIA1 STAM STS STX1B STXBP2 TAC1 TCF21 TEGT TLE3 TNFRSF11B TP53 TRAF1 TSPAN4 TUBB TUBG1 UGT8 UPF1 VGF WIPF1 YWHAH ZBTB17 ZYX FOTEMUSTINE 2 MGMT TXNRD1 (1+0) TXNRD1 (12.56) 12 56 12 56 627 (Muphoran)(50% DNA >> Alkylating agents >> Platinum CARBOPLATIN ALB BAX BCL2 BCL2L12 4 BCL2L12 (4.27) CASP3 (Paraplatin, Paraplatin- 11 BIRC2 CASP3 CASP9 (3+1) (2.14) PARP1 (2.68) 3.76 3.03 82 AQ) DCT FAS PARP1 RBM17 (36.4%) FAS (-5.93) CISPLATIN A2M ABCB1 ABCC1.54.ABCC.(7.7)AC (4.39, (Abiplatin, ABCC2 ABCC5 AKT1 (27+27) 5.45) ABCC5 (4.30, 6.53) 5.10 4.46 108 BRocisplatinum, Briplatin, AKT2 AKT3 ALB APEX1 (48.6%) AKT2 (2.40, 3.07, 2.01) WO 2011/003911 PCT/EP2010/059648 196 Carboquone, Cis Pt i, AXL BAX BCL2 BCL2L1 B.. CL2L12 (4.27) CASP2 Cismaplat, Cisplatine, BCL2L12 BIRC2 BIRC3 (2.27) CASP3 (2.14) CASP8 Cisplaty, Citoplationo, BIRC5 CASP2 CASP3 (8.85, 2.67) CREBBP (2.11, Lederplatin, Neoplatin, CASP8 CASP9 CAT 2.52) ETV4 (2.09) GSTP1 Plastin Platamine, CDKN1A CFLAR CLU (3.00) HSPE1 (5.96) KRT4 Platib/astin, Platidiam, CNTF CREBBP CYCS (8.00) LASS4 (5.10) MCM2 Platinex, Platinol, CYP3A4 DIABLO DPYD (3.39) MED1 (2.44) NCOA3 Platinol-AQ, Platinoxan, EDN2 EGFR EP300 (3.29,4.36, 3.16) NR112 Randa) ERBB2 ERCC1 ERCC2 (3.23) PARP1 (2 68) PMAIP1 ETV4 FADD FANCG FAS (12.40) PTK2 (2.54) FASLG FGFR3 FOSLI SLC31A1 (2.02, 2.41) GDF15 GSTM1 GSTM3 SPINT2 (6.04) TF (9.84, GSTP1 GSTT1 GUK1 8.91) TXN (4.58, 5.09) TYMS HIF1AHSPE1 HTRA2 (3.69) VEGFA (2.60) IFITM1 IGFBP3 IL4R ITGA9 ITGB4 JUN KRT19 A2M (-4.77) ABCB1 (-3.59) KRT4 LASS4 MCM2 AKT3 (-3.90, -3.96) BIRC3 ( MED1 MGMT MMP10 2.84) CAT (-3.31) CDKN1A ( MMP15 MMP16 MRPS27 7.85, -4.44) CYP3A4 (.7.84) MT1A MT2A MT3 MVP DPYD (-2.82, -4.07, -4 30) NAIP NCOA3 NOTCH4 EDN2 (-3.91) EGFR (-12.64) NR112 NTHL1 PARP1 FAS (-5.93) FGFR3 (-8.75) PCNA PDIA3 PFDN5 GSTM3 (-3.01) IL4R (-4.02) PGK1 PMAIP1 PRNP ITGA9 (-2.21) JUN (-2.62) PTEN PTGS2 PTK2 RB1 MMP10 (-2.05) MT1A (-7.47) RHOC RPL36 RPS5 MT2A (.8.59, -9.15) MVP ( RUNX3 SFN SLC31A1 2.25) NOTCH4 (-2.63, -3.15) SOCS1 SPINT2 TF TP53 PFDN5 (-2.17) PRNP (-3.95) TP73 TRAM1 TRAP1 PTEN (-2.30, -2.56) PTGS2 ( TUBA1ATXN TYMS 36.04, -32.79) SOCS1 (-5.85, VEGFA XIAP XPA XRCC1 -5.20) TRAM1 (-3.53) ZFP36L1 BCL2 BCL2L1 BIRC5 OXALIPLATIN CCNA2 CCNB1 CCNE1 CCNA2 (5.27) CCNB1 (3.17) (DACPLAT, Eloxatin, CC CDK2 CDKN1 B 8 CNl(.2 2(.2 F//At PLoToxatin, 17 EGFR CK CC2 (6+2) GSTP1 (3.00) TYMS (3.69) 5.20 3.95 139 Eilat, Foloxatine, :EGFR ERCC1 ERCC2 4 % Transplatin) GSTP1 IL8RA MYC RB1 (47)1-) TYMS EGFR (-12.64) 1IL8RA (-5.26) TYMS DNA >> Alkylating agents >> Alkyl sulfonates BUSULFAN (Busu/fex, Citosulfan Leucosu/fan, Mablin, Mielevcin, Mielosan, Mielucin, Mi/ecitan GSTA1 GSTM1 GSTP1 2 Mileran, Misulban, 5 M G(2+0) GSTA1 (16.89) GSTP1 (3.00) 9.95 9.95 397 Mitosan, Mitostan, (40.0%) Myeleukon, Mye/oleukon, Myelosan, My/ecytan, Myleran, Myleran Tablets) .DNA >> Alkylating agents >> Hydrazines PROCARBAZINE (Matulane, Nathulane, Natulan, Natulan 1 MGMT hydrochloride, Natulanar, Natunaar) DNA >> Alkylating agents >> Triazenes WO 2011/003911 PCT/EP2010/059648 197 .APEX1 BIRC5 CASP3 CASP3 (2.14) CASP8 (8.85, CASP8 CD38 CD69 2.67) MAP3K1 (2.06, 2.62) DACARBAZINE CYP1A1 CYP1A2 EPO 9 SLC2A1 (22.83) VEGFA c22 IFNA1 IFNA2 IL8 IRF1 (5+4) (2.60) 7.30 7.13 162 MAP3K1 MAPK1 MAPK3 (40.9%) 1L8 (-16.97) IRF1 (-2.23) MCL1 MGMT POLA2 MCL1 (-3.34) TNFSF10 ( SLC2A1 TNFSF10 VEGFA 7.51) AGT DCT MGMT MPG 3 TEMOZOLOMIDE AGT (3.57) PARP1 (2.68) 10 PARP1 PMAIP1 POLB (3+0) AT (.) 6.21 6.21 186 (Temodal, Temodar) POlPL P3(00)PMAIP1 (12.40) POLI POLL TP53 (30.0%) DNA > Alkylating agents > Aziridines THIOTEPA GSTA1 GSTA2 GSTM1 GSTA1 (16.89) GSTA2 (Thioplex, Thiotepa) 5 GSTP1 MGMT (1 16.8) GSTP3 11.90 11.90 713 (60.0%)(1.7165)GP130) DNA > Alkylating agents > Other ............................... ............................ ................... ..... .......................................... ....... ........ EY1 CCNB2 (4.19) E2F1 (2.45) (Trabectedin, ET-743 4 E2F1A2 CCNB1 CCNB2 (4+0) ECA 527)1OB 17) 77 3 77 376 Yondelis) (100.0%) DNA >> Spindle poisons / Mitotic inhibitors DNA >> Spindle poisons Mitotic inhibitors >> Taxanes ABCB1 ABCC1 ABCC2 ABCC1 (7.70) ABCC2 (4.39, BCL2 BCL2L1 BIRC5 5.45) CASP3 (2.14) CASP3 CFLAR CYP19A1 CYP19A1 (2.64) GSTP1 CYP1B1 CYP3A4 ERBB2 (3.00) PARP1 (2.68) VEGFA DOCETAXEL GSTP1 HIF1A HRAS ILS (260) (Taxotere) 33MAPK1 MAPK3 MDM2 ABCB1 (-3 59) CYP1B1 872 367 77 PA~i PAR~ PGR POR (45.5%) AC1(35)C~B PAR1 PARP1 PGR POR :3.87) CYP3A4 (-7.84) HRAS PTGS2 RAF1 RB1 SKP2 (-2.10) IL6 (.39.79) PGR ( TNF TNFRSF10B TP53 11.29) PTGS2 (-36.04, TUBB TUBB1 VEGFA 32.79) TNFRSF10B (-2.25) ABCB1 ABCC1ABCC2 ABCC1 (7.70) ABCC2 (4.39, AIFM1 AKR1C2 AKT1 5.45) AKT2 (2.40, 3.07, 2.01) AKT2 AKT3 AMFR APAF1 ASPM (5.55, 8.12) AURKA ARR3 ASPM AURKA BAD (4.00) BECN1 (2.18) CA12 BAX BBS4 BCL2 BCL2L1 (21.62, 17.07, 11.24, 25.25) BECN1 BID BIRC2 BIRC3 CASP3 (2.14) CASP8 (8.85, BIRC5 BMP7 BTG3 2.67) CCNB1 (3.17) CTNND2 C70RF23 CA12 CALCA (3.12, 2.44) CYP19A1 (2.64) CASP3 CASP7 CASP8 E2F3 (2.71) FTH1 (2.42, CASP9 CAV1 CCNB1 2.32, 2.35) FTL (3.51, 4.41, PACLITAXEL CCND1 CD46 CD47 3.40) GSTP1 (3.00) MED1 (Epitaxo LipoPac 2CDKN1A CFLAR CLU 65 (2.44) MED13L (6.09) MELK 12 CXC CTNND2 CTSL1 (6.04) NAT1 (2.39) NCOA3 Onxol, Paxceed, (31+34) 3.29,4.36, 3.16) NKIN1 6.54 6.14 147 Paxene, Taxol, Taxo/A CYCS CYP19A1 CYP2C8 (50.4%) (22.33) NR112 (3.23) PARP1 vascular Wrap, Xorane) CYP3A4 DIABLO E2F3 (2.68) PRC1 (3.44) RRM2 EGFR ERBB4 FAS FASLG (3.30, 3.47) S100P (41.69) FDPS FTH1 FTL FURIN TOP2A (5.88) TYMS (3 69) FXYD3 GADD45A GAMT VEGFA (2.60) ZNF552 (8.76, GFRA1 GSTM1 GSTM5 2.45) GSTP1 GSTT1 HIF1A ABCB1 (-3.59) AKT3 (-3.90, ICAM1 IF130 IGFBP4 3.96) AMFR (-5.37) BIRC3 ( ITGB5 JMJD2B JUN 2.84) BMP7 (-16.37, -4.33) KIAA1467 KIF3A KRT13 BTG3 (-7.01) CAV1 (-22.26, MAPK1 MAPK14 MAPK3 24.68) CCND1 (-2.52) CD47 MAPK8 MAPK9 MAPT (-6.41, .16.99) CDKN1A (- WO 2011/003911 PCT/EP2010/059648 198 .MCLI D2MD 7.85, -4.44) CXCR4 (-2.72) MED13L MELK METRN CXCR6 (-2.50) CYP3A4 ( MMP9 MT1E MT2A MYC 7.84) EGFR (-12.64) ERBB4 NAT1 NCOA3 NFKB1 (-3.94, -3.33) FAS (-5.93) NFKBIA NKAIN1 NR112 FURIN (-2.72) FXYD3 (-2.65) PARP1 PDPK1 PMP22 GAMT (-3.39, -2.43) GFRA1 POR PRC1 PTEN PTGS2 (-5.66) GSTM5 (-3.70, -6.54) RAMP1 RB1 RBL2 RELA ICAM1 (.8.24) IGFBP4 ( RFX5 RPL23A RPLP1 2.66) JUN (-2.62) MAPK8 ( RRM2 RTKN S100P 2.06) MCL1 (-3.34) MMP9 ( SCUBE2 TFF1 THNSL2 10.90) MT1E (-15.67) MT2A TNFRSF10B TOP2A TP53 (-8.59, .9.15) NFKBIA (-4.00) TUBB TUBB1 TYMS PMP22 (-6.22) PTEN (-2.30, VEGFA XIAP ZNF552 2.56) PTGS2 (-36.04, -32.79) TNFRSF10B (-2.25) DNA > Spindle poisons I Mitotic inhibitors > Vinca Alkaloids VINBLASTINE (Nincaluico/flastine' ABCC1 (7.70) ABCC2 (4.39, Vno/eVeban. Vebe' ABCB1 ABCC1 ABCC2 5.45) CASP2 (2.27) IKBKB Vinbastine VSufate, ' CASP2 IKBKB JUN 9 (2.08, 2.59, 3.48) e13 MAPK8 MAPK9 NFATC4 (4+5) 3.57 4.40 135 Vinbiastinum, Vincaeucobastin, NFKBIA TNFRSF10B (69.2%) ABCB1 (-3.59) JUN (-2.62) Vinca/eucob/astine' TP53 TUBB2A MAPK8 (-2.06) NFKBIA ( Vinca/eukob/astine, 4.00) TNFRSF10B (-2.25) Vincoblastine) ABCB1 ABC1 ABCC2 ABC (7.70) ABCC2 (4.39, AKT1 APAF1 APOA1 ASPM AUA ADAX 5.45) ASPM (5.55, 8.12) ASPM AURKA BAD BAX BCL2 CASP1 CASP10 AURKA (4.00) CASP2 (2.27) CASP2 CASP3 CASP4 CASP3 (2.14) CASP8 (8.85, CASP5 CASP6CASP7 2.67) CES1 (13.90) E2F1 CASP8 CASP9 CAT (2.45) GULP1 (21.16, 15.15) CEBPB CES1 CFTR CY'C1 MAP3K7 (3.78) PRC1 (3.44) VINCRISTINE E2F1 EGFR FADED FAIM2 RARB (2.96) SLC2A1 (22.83) (Marqibo, Onco TCS GAPDH GSTM1 GULP1 31 TYMS (3.69) XBP1 (2.26) Oncovin, Vincasar, VincasarPS VFncr,70 HRAS APP GF1 IGF1R (16+15 ABCB3 59) CASP1 6.79 6.69 152 Vincristine Sulfate PFS MPK MA493) CASP4 (-2.29, -2.55) :MAP2K4 MAP3K7 MAPK1 Vinkristin) MCASP5 (-2.47) CAT (-3.31) MYC MNFB MGMT PCEGFR (-12.64) HRAS (-2.10) MYC1 PN P P112 PCAFIGF1 (-5.82, -4.31, -5.16, PT1 PN RARB M175.25) IL6 (-39.79) LMNA ( RELAN3 RAS1 RNX3 2.45) PCAF (-4.52, -6.85) RELA ROSI RUNX3 SLC2A1 SOS1 STAT1 PRNP (-3.95) PTPN13 .STAT2 STAT3 TNF TP53 3.49) ROS1 (-5.49) VDR TUBB2A TYMS VDR XBP1 6.07) VINFLUNINE 1 TUBB1 VINDESINE (DAVA, Eldesine, 1 TUBB1 Eldisine) VINORELBINE (Navelbine, Navelbine 3 RBM17 SLC29A1 TUBB2A - Base) DNA >> Spindle poisons / Mitotic inhibitors >> Other .BAX BCL2 BCL2L1 BIRC3 3 BIRC3 (-2.84) MCL1 (-3.34) MCL1 TUBA1 TUBA2 (0+3) TUBB4 (-4.81) WO 2011/003911 PCT/EP2010/059648 199 ......... ...... ........... ... .. ....... .... ...... (17.... .6.............. ..... .......... .............. .. ...... . ........ . ...... TUBB1 TUBB2ATUBB2C TUBB3 TUBB4 TUBB4Q XIAP DNA >> Cytotoxic / Antitumor antibiotics DNA >> Cytotoxic / Antitumor antibiotics >> Anthracyclines ABCB1 ABCB1AABCB4 .ABCC1 (7.70) ABCC3 (8.66) ABCB5ABCC1 ABCC3 AKR1C3 (3.54) AKT2 (2.40, ABCG2 ADAM19 3.07, 2.01) ATM (2 79) ADAMTS5 AHCY AIFM1 BCL2L11 (3.13) BCL2L12 AKR1A1 AKR1C2 AKR1C3 (4.27) BECN1 (2.18) BIK AKT1 AKT2 AKT3 ALB (3.06) BUB1B (12.77) CA12 AMER ANGPT1 AP4E1 (21.62, 17.07, 11.24, 25.25) APAF1 API5 APOA1 APP CABC1 (3.10) CASP2 (2.27) ARHGEF1 ARHGEF17 CASP3 (2.14) CASP8 (8.85, ARL6|P5 ATF4 ATM 2.67) CBR1 (6.01) CCNA2 ATP6V0E1 AZGP1 (5.27) CCNB1 (3.17) CCNE1 B4GALT1 BACH1 BAD (5.62) CDC2 (2.92) CDC25C BAG1 BAK1 BAX BBS12 (8.00) CDKN2A (18.18, 9.22) BBS4 BCL2 BCL2A1 CENPA (3.86) CES1 (13.90) BCL2L1 BCL2L11 CHEK1 (4.69) CHEK2 (2.55) BCL2L12 BECN1 BID BIK CLIP1 (2.08) CPT1A (2.21) BIRC2 BIRC3 BIRC5 CREBBP (2.11, 2.52) BIRC6 BIRC7 BIRC8 CTNND2 (3.12, 2.44) BTG1 BTG2 BTG3 BUB1B CYP2B6 (10.92, 3.17) DEK BUB3 C210RF87 (2.71) DNMT1 (2.20) DTYMK C5ORF13 C70RF23 CA12 (2.69, 2.60) E2F1 (2.45) DOXORUBICIN CABC1 CALD1 CAPN6 E2F3 (2.71) EGF (16.93) (ADM, Adriablastin, CASP1 CASP10 CASP2 EHMT2 (4.08, 2.31, 2.75) Adnamycin, Adriamycin CASP3 CASP4 CASP5 ERBB3 (2.54, 3.81 4.45) PFS, Adnamycin RDF, CASP6 CASP7 CASP8 ETV6 (2.09) FOXO3 (2.21) Adnamycin 4CASP9 CBR1 CCL2 198 FTL (3,51, 4.41, 3.40) G6PD Semiquinone, 4CCNA2 CCNB1 CCND1 (97+101) (5.12) GSTA1 (16.89) GSTP1 5.97 5 64 123 Adriblastin, Adiblastina, CCND2 CCNE1 CD44 (44.6%) (3.00) GULP1 (21.16, 15.15) Caelyx, DM2, Doxii, CDC2 CDC25C CDK10 HCRTR1 (2.33) HDAC1 Doxo, Myocet, RDF CDK2 CDKN1A CDKN1B (6.44) HFE (3.86, 3.08) Rubex, Resmycin, CDKN2A CEBPB CENPA HSAJ2425 (2.32) HSPA9 Rubex) CES1 CFLAR CFTR (2.34) HSPB1 (4.23, 3.70, CHEK1 CHEK2 CHUK 3.27) HSPD1 (3.65, 5.01) CISH CLDN1 CLIP1 IKBKB (2.08, 2.59, 3.48) ILF3 CLPTM1 CLU COL18A1 (3.78) KCNH2 (4.05) KRT18 COX2 CPT1A CREBBP (3.49, 2.64, 3.00, 2.48) LY75 CRISP2 CRYAA CRYAB (4.92) MAD2L1 (3.87) CSPG4 CTGF CTNNBIP1 MAP3K7 (3.78) MDK (17.02) CTNND2 CTSB CTSD MED13L (6.09) MELK (6.04) CTSG CXCL6 CYC1 MLL (2.05, 2.04) MME (3.38, CYCS CYP1A1 CYP27B1 4.93) MRPS18A (2 34) CYP2B6 DDEF1 DDIT3 NKAIN1 (22.33) NOLC1 DEK DGKI DHPS DIABLO (2.54, 2.56) OXTR (4.19) DKK2 DNAJA1 DNMT1 PARP1 (2.68) PDCD5 (3.33) DPP4 DSPP DTYMK E2F1 PMAIP1 (12.40) PRKDC E2F3 ECHS1 ECOP EDN1 (2.47, 2.93) PROC (27.80) EFNB3 EGF EGFR EGR1 PSMB7 (2.15) PTK2 (2.54) EHMT2 EIF1AX EIF2A PTPRH (42.24) RAD51 (4.60) EIF4EBP1 EP300 EPHA2 RARB (2.96) RHCG (3.15) ERAP1 ERBB2 ERBB3 RRM2 (3.30, 3.47) SMAD3 ERBB4 ESR1 ESR2 ETV6 (4.63,3.78) SOD1 (2.68, F3 F7 FADD FAIM2 FAS 2.37) SSH3 (3 00, 3.18) ST14 FASLG FCGR3A FDFT1 (2.22) STK39 (6.44, 6.78) WO 2011/003911 PCT/EP2010/059648 200 FHL2 FKBP5 FKBP8 FN1 TCF3 (4.52, 3.83) TERT FOLR1 FOS FOXJ1 (3.67) TNFRSF25 (7.01) FOXO1 FOXO3 FSHB FTL TOP2A (5.88) TRIM28 (2.33) FTMT FUT4 G6PD TUBA4A (2.16) TYMS (3.69) GADD45G GAMT GAPDH UQCRH (3.18,3.28) VEGFA GATAl GDF15 GFI1B (2.60) ZMYM2 (3.03, 4.70) GFRA1 GLO1 GNRHR ZNF552 (8.76, 2.45) GP9 GPX1 GSK3B GSTA1 ABCB1 (-3.59) ADAMTS5 ( GSTA4 GSTP1 GTF3C1 3.42) AKT3 (-3.90, -3.96) GULP1 H2AFX HBG2 AMFR (-5.37) ANGPT1 ( HCRTR1 HDAC1 HFE 13.77, -19.66) APP (-3.45) HGF HIFlA HIST1H2BC ARL6|P5 (-3.81, -3.50) HLA-DQB1 HLA-DRB4 ATP6V0E1 (-4.06) BCL2A1 ( HMP19 HNRNPM HRAS 18.59, -2.78) BIRC3 (-2.84) HS3ST1 HSAJ2425 .BTG2 (-3.68) BTG3 (.7.01) HSP90AA1 HSPA4 HSPA5 CAPN6 (-3.15) CASP1 ( HSPA8 HSPA9 HSPB1 4.93) CASP4 (-2.29, -2.55) HSPD1 HTRA1 HUWEl CASP5 (-2.47) CCL2 (-8.86) APP IFNAl IGF1 IGF1R CCND1 (-2.52) CCND2 ( IGFBP4 IKBKB IKBKG IL6 7.50, -3.63, -13.98) CD44 ( 1L8 ILF3 IRS1 JAG2 5.46) CDKN1A (-7.85, -4.44) JMJD2B JUN KCNH2 CISH (-6.49, -6.90) CRISP2 KCNJ13 KCNJ16 KDSR (-3.03) CRYAB (-3.91) KHSRP KIAA1467 KIF3A CSPG4 (-4.23) CTGF (-2.88) KLF2 KRT18 LGALS3 CTNNBIP1 (-7.17) CTSB ( LIG4 LIMAl LMNA LMNB1 .2.39) CTSD (-2.66) CTSG ( LY75 MAD2L1 MAP2K1 8.64) CXCL6 (.7.14) DKK2 ( MAP2K2 MAP2K3 2.84, -19.13) DPP4 (-21.02) MAP2K4 MAP2K6 EDN1 (-4.13) EFNB3 (4.49) MAP3K7 MAP3K8 MAPK1 EGFR (-12.64) EGR1 (-6.04) MAPK14 MAPK3 MAPK8 ERBB4 (-3.94, -3.33) F3 ( MAPK9 MAPT MCL1 3.63) FAS (-5.93) FCGR3A ( MDH2 MDK MDM2 2.85) FN1 (-3.36, -3.49, MED13L MEF2A MELK 4.91) FOXJ1 (-5.38, -2.05) METAP2 METRN MFGE8 FOXO1 (-2.47, -3.13) GAMT MGMT MLL MLLT3 MME (-3.39, -2.43) GFRAl (-5.66) MMP1 MMP2 MRPS18A GP9 (-2.54) HGF (-2.73, MSH6 MT1G MT1L MT2A 6.54) HLA-DQB1 (-3.19, MTR MTSS1 MVP MYC 3.83) HLA-DRB4 (-4.14) NAIP NBN NDUFB7 HRAS (-2.10) HSPA5 (-2.00) NFKB1 NFKB2 NFKBIA IGF1 (-5.82, -4.31, -5.16, NID1 NKAIN1 NMBR 5.25) IGFBP4 (-2.66) IL6 ( NOLC1 NOS2 NR4A1 39.79) IL8 (-16.97) IRS1 ( OR12D2 OR2F1 OXTR 13.62) JUN (-2.62) KCNJ16 ( P11 P2RX2 P2RY6 14.12) KDSR (-2.45) KLF2 ( PARP1 PAX6 PCAF PCNA 3.43, -2.31) LMNA (-2.45) PDCD5 PDPK1 PITX1 MAP2K3 (-3.18, -3.56) PLAU PLOD1 PLOD3 MAP2K6 (-4.53) MAPK8 ( PMAIP1 POLD4 POMT2 2.06) MCL1 (-3.34) MMP1 ( POR PPARD PPT2 58.24) MMP2 (-4.48) MT1G ( PRKACA PRKCA PRKCD 11.42, -6.38) MT1L (-11.08) PRKDC PRL PRNP PROC MT2A (-8.59, -9.15) MTSS1 PROCR PSMA1 PSMA2 (-2.60) MVP (-2.25) NFKBIA PSMA3 PSMA4 PSMA5 (-4.00) NR4A1 (-5.37) PCAF PSMA6 PSMA7 PSMB2 (-4.52, -6.85) PLOD1 (-2.05) PSMB3 PSMB7 PTEN PRKCA (-3.26) PRNP (-3.95) PTGS2 PTK2 PTK2B PROCR (-2.63) PTEN (-2.30, PTPN13 PTPRH RAD51 -2.56) PTGS2 (-36.04, RALBP1 RAMP1 RARB 32.79) PTPN13 (-3.49) RELB RB1 RBL1 RBL2 RBM17 (-2.34) ROSi (-5.49) WO 2011/003911 PCT/EP2010/059648 201 RBPMS REL RELA RELB SERPINE1 (-2.71) RGS13 RHCG ROS1 SLC22A16 (-13.33) SLIT3 ( RRM2 RUNX1 RUNX3 4.84) SMAD2 (-2.15, -3.51) SAT SCNN1G SCUBE2 SMAD7 (-2.99) TGM2 (.6.85, SEC22B SERPINE1 -4.62) THBD (.5.54) THBS1 SERPINH1 SKP2 (-2.30, -3.06) TMSB4X ( SLC22A16 SLC22A5 2.05) TNFRSF10B (-2.25) SLC29A1 SLC38A2 TNFSF10 (-7.51) TRIM34 ( SLC5A5 SLC9A1 SLIT3 3.03) TUBB4 (-4.81) UPP1 ( SMAD2 SMAD3 SMAD4 3.24, -2.71, -2.28) VDR ( SMAD7 SMC1A SOD1 6.07) ZFP36L2 (-2.99) 3002 SOS1 SP1 SPARC SPHK2 SRM SSH3 ST14 STAT1 STAT2 STAT3 STK39 STMN1 STX3 TAGLN TCF3 TERT TFF1 TGFB1 TGM2 THBD THBS1 THNSL2 TIA1 TLX3TMSB4X TNF TNFRSF10A TNFRSF10B TNFRSF1A TNFRSF1 B TNFRSF25 TNFSF10 TOMM20 TOP2A TOP2B TP53 TP73 TRAP1 TRIM23 TRIM28 TRIM34 TRP53 TTPA TUBA4A TUBB TUBB4 TYMS UGCG UGT2B4 UPP1 UQCRH VDR VEGFA XIAP ZFP36L2 ZMYM2 ZNF22 ZNF552 EPIRUBICIN (Ellence Epi-Dx, Epiadriamycin, Epidoxorubicin, Epirubicina, Epirubicine, ABCB1 ABCC1 ABCG2 4 AB (7.70) CASP3 (2.14) :TOP2A (5.88) Epirubicinum, IMI 28, 7 BIRC5 CASP3 CHD1 (3+1) 4.83 5.24 224 Pharmorubicin Pfs, TOP2A (57.1%) ABCB1 (-3.59) Pidorubicina, Pidorubicine, Pidorubicinum, Ridorubicin) DNA >> Cytotoxic / Antitumor antibiotics >> Anthracenediones ABCB1 ABCG2 AMOTL1 .BIC CASC32 L..... CASP2 (2.27) ELAVL4 (8.24, ELC AVL4ELOVL5:12.95) RARB (2.96) SYTL2 .ELAL4 EOVL5(10.42, 7.94) TMEM99 (2.94) MITOXANTRONE EPB41L2 FAM130A1 IRX1 11 (Mitox, Novantron, 25 LAMA2 LEMD2 MAFF (6+5) TOP2A (5.88) 5.46 6 64 135 Novantrone) MDM2 PACRGL RARB (44.0%) RASA1 RBM17 RRAGD SFRP4 SYTL2 TMEM99 9.67, -12.72) IRX1 (-2.23) .TOP2AXIAP LAMA2 (-5.62) MAFF (-3.57) DNA >> Cytotoxic I Antitumor antibiotics >> Streptomyces ..... ..... ............... ............................... .. .................... ... G............................. ..... ). ...... ...... BLEOMYCIN BIRC2 BLMH CAT LIG1 (Bleo Bleonexane) LIG3 SOCS1 XRCC1 (2+2) CAT (-3.31) SOCS1 (-5.85, - 3.53 2 65 75 (57.1%) 5.20) MITOMYCIN ALDH3A1 APP ATF4 13 ALDH3A1 (12.05) BMP4 (Ametycin, Ametycine, BMP4 CCNB1 CD59 (8+5) (3.33) CCNB1 (3.17) GSTP1 WO 2011/003911 PCT/EP2010/059648 202 Mit-C, Mito-C, Mitocin- EGR1 EPHA2 GSTP1 (52.0%) (3.00) LIG1 (3.03) NFYA C, Mitomycin C, HLA-C LIG1 MAPRE1 (2.84) NQO1 (18.75) RFC4 Mitomycin-C, MGMT MMP14 NFYA (2.95) Mitomycinum, NQO1 POR PRNP RB1 APP (-3.45) CD59 (-8.71, Mitomycinum C, RBBP7 RFC4 RPS3A 4.89, -4.77, -7.00) EGR1 ( Mitomycyna C, RUNX3 SUB1 TP53 6.04) PRNP (-3.95) RPS3A ( Mitozytrex, Muamycin, 2.73, -2.31, -2.81, -2.51, Mutamycin, Mytomycin 2.45, -2.68) Mytozytrex) DNA >> Cytotoxic / Antitumor antibiotics >> Other HYDROXYUREA BP CASP3 (2 14) CCNA2 (5.27) BAXCIORF34ASP3CONBi (3.17) CONE1 (5.62) (Biosupressin, Droxia, CCNA2 CCNB1 CCND1 Hidrix, Hydrea, Hydreia, CCND2 CCND3 CCNE1 13 Hydura, Hydurea, 21 CCNG1 CDCA8 CDKN1A (8+5) CCND (.44) CCND (- 4 64 4 27 162 Litaler, Litalir, Onco- CKS2 EDN3 FAS KPNA2 (61.9%) 7.50 -2.52 ) CN 1 ( Carbide, Oxyurea, PRC1 PSRC1 RRM1 TP53 363 ) CN. 3.23) CKNIA (.7.85, -4.44) Ureaphil) UBE2C FAS (-5.93) DNA >> Topoisomerase inhibitors DNA >> Topoisomerase inhibitors > Camptotheca ABCB1 AGRN ALB ANXA4: ABO~iAGRNALB NXA4CASP2 (2.27) CASP3 (2.14) BAX BCL2 BIRC5 CAB39 CASP2 CASP3 CCNB1 CDK2 CDKN1ACEACAM1 (2.43, 2.23) GSTR (3.00) CEBPZCTSBE124PARPi (2.68) PRC1 (3.44) CEBPZ CTSB E124 EPM2AIP1 FDXRGSTP1 2 VEGFA (2.60) CAMPTTHECN 47HNRNPC IL B IL8 JUN 2 CAMPTOTHECIN 47AP3K5 APK9 (8+12) ABCB1 (-3.59) CDKNIA (- 4.28 2 70 46 MDM2 NCK2 PARP1 PLK3 (42.6%) 7.85, -4.44) CTSB (-2.39) PPP1R1B PRC1 RB1 IL1B (.12.47) IL8 (-16.97) RBL1 RBL2 SDC1 TAP1 JUN (-2.62) MAP2K3 (-3 18, TAX1BP3 THBS1 TNFSF9 3.56) PLK3 (-2.94) PPP1R1B TOP1 TP53 TP5313 (-5.77) SDC1 (-2.52) THBS1 T P53TG1 VEGFAXIAP (-2 30, -3.06) TNFSF9 (-2.43) TOPOTECAN ABCG2 ARNT H2AFX 1 (Hycamptamine, 7 HIFA TOPI TOP1MT (1+0) VEGFA (2.60) 2.60 2.60 37 Hycamptin, Hycamtin) VEGFA (14.3%) ABCB1 CClA1 ABCC2 ABCC1 (7.70) ABCC2 (4.39, ABCC4 ABCG2 ADD3 5.45) ABCC4 (2.34) AURKB AKAP10 ALAS2 ALDH1Al (2.61) BCL9 (5.39) BUB1 ANGPTL2 ANXA6 APC (2.75) CCNA2 (5.27) CCNB2 ARAF AREG ARHGAP5 (4.19) CCNF (2.52) CCR6 ARID4AATF3 ATG10 (2.28) CDC2 (2.92) CDKN3 ATP50AURKB BCL7A (5.50) CENPF (7.39, 8.14) BCL9 BIRC5 BMP8A CES1 (13.90) CES3 (10.19) IRINOTECAN BOLL BUB1 CASP4 1 CREBBP (2.11, 2.52) 20 CCNA2 CCNB2 CCNF CYP2B6 (10.92,317) (CPO, Camptosar' 4 CCR1 CCR6 CD70 CD80 (53+51) CYP2C9 (3.97) CYP2D6 759 654 169 IRINOTECAN. CPT-11) 0002 CDC25B CDC2L5 (51.0%) (3.52, 3.15) DENND4A (2.86, CDK5R2 CDKN1A CDKN3 2.30) DYRK2 (3.69, 2.90, CENPE CENPF CES1 2.19, 2.26) EML2 (4.55) CES2 CES3 CHD1 ETV6 (2.09) GATA3 (6.10) COL6A1 COMMD6 GOLGA8A (8.56) MAD2L1 CREBBP CYP1A2 (3.87) MCM4 (4.63) MX2 CYP2A6 CYP2B6 (2.64) NEK2 (23.93, 26.08) CYP2C19 CYP2C8 PLEKHH3 (2.59) PLK1 (2.97, CYP2C9 CYP2D6 CYP2E1 2.70) PMAIP1 (12.40) WO 2011/003911 PCT/EP2010/059648 203 CYP3A4 CYP3A5 PROM (4.29) PTGES (9.53) DCLRE1A DDB2 DEFA1 RAC3 (2.23) RAD23B (2.57, DENND4A DMWD DNMT2 2.55, 4.15) RBBP5 (2.08, DPYD DRD5 DRG1 2.99) RFC3 (4.94, 4.23) SPIB DUSP2 DYRK1A DYRK2 (4.00) STK38 (4.45, 4.62) EBI2 EGFR EGR1 EML2 STK4 (2.65) TAF2 (2.17, EP300 ERBB4 ETS2 ETV6 2.48) TBCC (2.21, 2.21) F8 FANCG FASLG FGF2 TOB1 (2.20) TOP2A (5.88) FN1 FOS FOSB GATA3 TPX2 (5.74) TTK (20.36) GDF15 GOLGA8A TYMS (3.69) UBE2C (9.47) GPR109B GPX3 H2AFX UGT1A6 (35.81, 53.16) HBEGF HIST1H2AC UGT1A8 (30.25) YES1 (2.33) HMMR HRG HSPA4L ID2 ZFX (2.61) IGF1 IL1B IL8 ITGAV ABCB1 (-3.59) ALAS2 (-5.81, ITGAX ITGB3BP JUN -8.89) ANGPTL2 (-2.89, KLF9 KRT5 LGALS8 LTF 3.89) ANXA6 (-3 31) AREG (: MAD2L1 MAL MALL 119.30) CASP4 (-2.29, -2.55) MAP3K5 MCF2L MCM4 CCR1 (-2.53, -4.69) CDKN1A MDM2 MEF2B MGMT (-7.85, -4.44) COL6A1 (-3.54) MMP9 MPO MSH2 CYP3A4 (-7.84) CYP3A5 ( MTHFS MX2 NCK1 . 34.94) DPYD (-2.82, -4.07, NCOA1 NEK2 NID1 OSMR: 4.30) EB12 (-5.77) EGFR ( PCDHGC3 PDE4B 12.64) EGR1 (-6.04) ERBB4 PLEKHH3 PLK1 PLK2 (-3.94, -3.33) ETS2 (-3.94, PLK3 PMAIP1 POR 5.62, -5.40) F8 (-3.51, -2.90) PPP3CB PRKCA PRKCB .FGF2 (-9.80) FN1 (-3.36, PRKCD PRKCI PROM1 3.49, -4.91) GPX3 (-4.85) PSG9 PTGES PTPN13 HBEGF (-5.28, .5.37) PTPN22 PTPRD PTPRN HSPA4L (-2.34) ID2 (-2.96, RAC3 RAD23B RB1 3.38) IGF1 (-5.82, -4.31, RBBP5 RBL1 RELA RFC3 5.16, -5.25) IL1B (-12.47) 1L8 RGS1 RUNX1 SDC4 (-16.97) ITGAX (-2.53, -2.13) SERPINE2 SESN2 SFN ITGB3BP (-2.46) JUN (-2.62) SGK SLC16A4 SLC9A3R2 KLF9 (-4.25) KRT5 (-12.89) SNAG1 SPIB STK38 STK4 LGALS8 (-2.28) LTF (-20.86) SULT1A4 TAF2 TBCC MAL (-7.99) MALL (-2.75, TGFA THY1 TMEM41 B 2.73) MMP9 (-10.90) OSMR TMSB4X TNFAIP6 TOB1 (-5.11, -3.52) PDE4B (-5.96) TOB2 TOP1 TOP1MT PLK3 (-2.94) PPP3CB (-2.30) TOP2A TOPORS TP53 PRKCA (-3.26) PTPN13 ( TPX2 TRADED TRAF1 TTK 3.49) PTPRD (-6.35) SESN2 TYMP TYMS UBE2C (-2.98) SLC16A4 (-7.88) UCHL5 UGT1A1 UGT1A1O TMEM41B (-2.16) TMSB4X ( UGT1A6 UGT1A7 2.05) TNFAIP6 (-22.06) UGT1A8 UGT1A9 XRCC4 TYMP (-3.83, -5.95) ZFP36L2 YES1 ZFP36L2 ZFX (-2.99) ZNF32 ZNF582 ZRSR2 DNA >> Topoisomerase inhibitors >> Podophyllum ABCB1 ABCC1 ABCC2 ABCC1 (7.70) ABCC2 (4.39, ABCC3 AGRN AKT1 5.45) ABCC3 (8.66) ALDH3A1 ANXA4 AREG ALDH3A1 (12.05) BIK (3.06) ETOPOSIDE ATF4 BAK1 BCL2 BCL2L1 BRCA1 (3.05) CASP2 (2.27) (Eposin, Etopophos, 1BCL2L2 BIK BIRC2 BIRC3 53 CASP3 (2.14) CCNA2 (5.27) Lastet, Toposar, 10 BIRC5 BRCA1 BTC (27+26) CEACAM1 (2.43, 2.23) 8.65 6 31 167 2 Vepesid, Vepesid J, CAB39 CALCA CASP2 (52.0%) CYP2B6 (10.92, 3.17) Zuyeydal) CASP3 CASP7 CASP9 CYP2C9 (3.97) EGF (16.93) CCNA2 CD44 CDK2 ERBB3 (2.54, 3.81, 4.45) CDKN1A CDKN1B ETV6 (2.09) FOXO3 (2.21) CEACAM1 CEBPZ CFLAR GSTP1 (3.00) PARP1 (2.68) WO 2011/003911 PCT/EP2010/059648 204 CYP27B1 CYP2A6 PRC1 (3.44) RAD52 (7.35) CYP2B6 CYP2C8 CYP2C9 RAD54L (8.38) TOP2A (5.88) CYP3A4 CYP3A5 DDIT3 TYMS (3.69) UBE2C (9.47) EGF EGFR E124 UGT1A8 (30.25) WRN (3.29) EPM2AIP1 ERBB2 ERBB3 XRCC2 (5.72) ERBB4 EREG ETV6 FDXR FOXO1 FOXO3 GSTM1 ABCB1 (-3.59) AREG ( GSTP1 HBEGF HSPA5 11930) BIRC3 (-2.84) BTC ( MAP2K3 MAP2K7 3.47, -22.42) CD44 (-5.46) MAP3K5 MCL1 MDM2 CDKN1A (-7.85, -4.44) NCK2 NFKBIA PARP1 CYP3A4 (-7.84) CYP3A5 ( PLK3 PRC1 PRNP PTEN 34.94) EGFR (-12.64) ERBB4 RAD52 RAD54L RBM17 (-3.94, -3.33) EREG (-25.83) RUNX1 RUNX3 SDC1 FOXO1 (-2.47, -3.13) HBEGF TAP1 TAX1 BP3 TGFA (-5.28, -5.37) HSPA5 (-2.00) TMSB4X TNFRSF10A MAP2K3 (-3.18, -3.56) MCL1 TNFRSF10B TNFSF10 (-3.34) NFKBIA (-4.00) PLK3 TNFSF9 TOP2A TOP2B (-2.94) PRNP (-3.95) PTEN ( TP53 TP5313 TP53TG1 2.30, -2.56) SDC1 (-2.52) TP73 TYMS UBE2C TMSB4X (-2.05) TNFRSF10B UGT1A1 UGT1A3 (-2.25) TNFSF10 (-7.51) UGT1A8 VDR WRN XAF1 TNFSF9 (-2.43) VDR (-6.07) XIAP XRCC2 XRCC3 XRCC4 DNA > Other DNA > Other >> All AKR1C1 (31.20,14.60, ADD3 ARB2 RC19.24, 4.57) AKR 1C3 (3.54) AR1C3PSAMOTLN2 CYP26A1 (99.46) KRT15 CAR|P5ASN CIP2R (2.99) PER2 (3.13) PNO1 C100RF10 C100RF1 16 34)RR 29)RR CEBPG COX2 CTH CTSH (3.43) RARB (2.96) RXRB CYCS CYP26A1 CYR61 SLC7A5 (2.98) UAP1 (2.13) DDIT4 DHRS3 DKK1 XBP1 (2 26) .DUSPi DUSP5 EGR3XB1(.6 DUSP1 P2 GR ADRB2 (-10.39) AMOTL2 ( GEM6B EP GS 2.31) ARL6|P5 (-3.81, -3.50) BEXAROTENE RGP HPUD1 IDCB6 39 ASNS (-3.65) CTH (-4.18) (Targret, Targretin, 6CTSH (-3.47) CYR61 (-3.69 Targretin-gel, Targretyn KT15 KR LO (12.4%) 2.81 -3.06) DDIT4 (-2.17) Targrexin) MAF MAFF MLL )DKK1 (.16.44) DUSP1 (-5.64) MMP1 MTFD2 OD1 GPM6B (-11.99) 1 (-2.07) MPR MTFD2 PLAR IGFBP6 (-3.88) IL15 (-2.46) PER2 PLAT PLAUR PLOD2 PNO1 PNRC1 ITGB6 (-21.40) ITM2A (-3.63) RARB RB1 RXRB S100A9 ITPR1 (-2.44) KLF10 (-2.47) SCD SFRS2 SIAH2 KRT17 (-5.21) MAF (-2.53, SLC7A1 SLC7A5 SMAD5 3.02) MAFF (-3.57) MMP1 ( SPARC TGM2 TM4SF1 58.24) PLAUR (-5.48) PLOD2 VAMP8 (-2.46) S10OA9 (-2.26) SCD XBPD A(-8.09, -15.46, -10.15) TGM2 (-6.85, -4.62) Cellular Cellular >> Cl monoclonal antibodies Cellular >> Cl monoclonal antibodies >> Receptor tyrosine kinase CETXtMAB 1 EGFR (0+1) EGFR (-12.64) 12 64 0 00 0 (Erbitux) (0.% (100.0%) TRASTUZUMAB 2 :EGFR ERBB2 1 :EGFR (-12.64) :12.64 0.00 0 WO 2011/003911 PCT/EP2010/059648 205 (Hacetin (0+ ........................ . ................................................................................. ( 1................................... . Cellular >>C1 monoclonal antibodies >> Anti-CID2O RITUXIMAB RITUXAB 1 MS4A1 (1+0) MS4A1 (7.61) 7.61 7.61 761 (Rituxan )0.% TOSITUMOMAB I............ TOIUOAB 1 MS4A1 (1+0) MS4A1 (7.61) 7.61 7.61 761 (Bexxar) 100 (100.0%) Cellular > C1 monoclonal antibodies >> Other ALEMTUZUMAB I (Campath, 1 CD52 (0+1) CD52 (-5.80) 5.80 0.00 0 MabCampath) (100.0%) EAIZUMB 6 VEGFA (1+0) KDR0 (2.6) 2T0 2 60 259 PDGFRA ~ ~ ~ ~ ~ VEF PDFB233%.860DGR)(421 (Avastin) (0 % EDRECOLOMAB 1TASD (Panorex) BOSUTINIB 1 0033 (0+1) 033 (-3.02, -3.56) 3.29 0.00 0 (Mylotarg) (0.% (100.0%)BC(-.0 Cellular > Tyrosine kinase inhibitors Cellular >> Tyrosine kinase inhibitors >> All CFLT1 (-2.68) FLT4 (-2.80) DASTINIB AL1 FLT4 KR KIT 6 YS (2 33) PyGFKA PDGFRB (0+3) BCR (-2.85, -2.5) KIT 8.12 0 P(83.3% PGR 38.04) PDGFRA (-421, 4.58) 3 BOSUTINIB 3 ABL BR SRC (2+1) ABFR (3.8) 2.69 .84 189 (100.0%) CEDIRANIB2 (Rcni) 2 ELiFLT4 (0+2) EFLTI (-2.68) FLT4 (-2.80) 2.74 0.00 0 (100.0%) AANBAVN CR HABI (3.58) SRC (2.09) DSCYP1A2 CYP2C19 (2.) C11 FYN KIT LCK PDGFRB (3+3) 8(2.) 267 72 (SpIycel) CPA CA5 DSP 22 CR (-2.40) FYN (-2.59) KIT (-38.04) ERLOTINIBI (1rea EGR (0+1) EGR (-12,64) 12 64 0200 0 (100.0%) .ABCG2 ADORA1 AREG. CYP2F9 (3.97) CYP26. AVNCGRRF1 (3.52, 3.15) EGF (16.93) COL4A3BP COR(8 G.L8 (7.00. 3.83) NRL CYPIA2 CYP2C19 :(2.45) DYP2D9 YP26 CYP2F1 ADORA1 (-2.74) AREG 2CYP3A4 CYP3A5 DUSP3 22 119F30 COL4A3BP (-2.58) DUSP9 EG EGFR EPOR COR01 (-3.41,.5.23) 32 MPK3M H12 5+17) CYP2F1 (-2.63) CYP3A4 Tarceva) FG6GDO5 )7.84) CYP3A5 (-34.94) EGFR: :GADD45G GARS GCLC (-12.64) HBEGF (.5.28, GNB2 GUCY2D HBEGF :5.37) IF16 (-7.89) 11L8 (.16.97) 1F1-I8 LEPR MAPK1 LEPR (-22.59, -4.51) NPTX2 MAPK3 MLH1 NFKB1 (-111.45) OSMR (.5.11, -3.52) ,NPTX2 NRL OSMVR :PHLDA2 (-3.07) OSOXi ( WO 2011/003911 PCT/EP2010/059648 206 PHLDA2 QSOX1 RBM7 4.99) SKI (-4.75, -2.71, -3.52) RPA1 SFN SKI TGFA TNFRSF1B ABCB1 ABCG2 ABL1 AKT1 BCL2L1 BCR BIRC5 ABL1 (3.58) DDR1 (3.07 CCND3 0069 CSF1R 2.69, 3.21) VEGFA (2.60) DDR1 FGFR3 FRAP1 1 ABCB1 (-3.59) BCR (-2.40) IMATINIB FGFR3 (-8.75) IGFI (-5.82, IMATINB G 29 HMOX1 IGF1 IL2RA KIT (3+8) 7.04 3.06 31 (Geevec,LCKMAPK1 MAPK3 (379%) 4.31,-5.16, -5.25) IL2RA( NFKB1 NTRK1 PDGFRA 2.77, -4.16) KIT (-38.04) RB1 RELAY RET PDGFRA (-4.21, -4.58) WT1 .PDGFRB(-2.49) VEGFA WT1 AKT1 BIRC5 CCND1 3CCNE1 (5.62) LAPATINIB CCNE1 CDK2 CDKN1B (Tycerb, Tykerb) EGFR ERBB2 ESR1 2) CCND1 (-2.52) EGFR ( MAPK1 MAPK3 12.64) 1 12.64) LESTAURTINIB 1 FLT3 (0+1) FLT3 (-8.47) 8.47 0 00 0 (100.0%) 4 ABL1 (3.58) NILOTINIB ABL1 BCR KIT PDGFRA 5 (1+3) 12 11 3.58 71 (Ketek) PDGFRB BCR (-2 40) KIT (-38.04) PDGFRA (-4.21, -4.58) SEMAXANIB 1 KDR (0+1) KDR (-2.85, -2.51) 2.68 0 00 0 (100.0%) ............................ ..... .............................. ................... ........... ... .......... . .~ ............................ ..... BAK1BAXBCL BC2L1BCL2L1 1 (3.13) BRAF (2.27) BCL2L B BL2 B9L2LCASP3 (2.14) CASP8 (8.85, SORFEIBBCL2L11 BID BRAF 9 (ORAFENIB 18 CASP3 CASP8 CYCS (4+5) 2.67) 763 3.32 73 FL3 LT KR ITMOl 500% FLT3 (-8.47) FLT4 (-2.80) FLT3 FLT4 KDR KIT MCL1 (50.0%) PKDR (-2.85, -2.51) KIT ( PDGFRB RAFI XIAP380)Ml(-.4 38.04) MCL1 (-3.34) BRAF (2 27) SUNITINIB BRAF CSF1R FLT1 FLT3 7 FLT1 (-2.68) FLT3 (-8.47) (SU-11248 Sutent) 11 FLT4 KDR KIT PDGFRA (1+6) FLT4 (-2.80) KDR (-2.85, - 8.76 2.27 20 PDGFRB RAF1 RET (63.6%) 2.51) KIT (-38.04) PDGFRA (-4.21, -4.58) VANDETANIB 2 EGER (-12.64) KDR (-2 85, 2 EGEIR KOR (0+2) 7.66 0.00 0 (Zactima) (100)2.51) (100.0%) Cellular >> mTOR inhibitors Cellular >> mTOR inhibitors >> All TEMSIROLIMUS CCND1 EIF4EBP1 ESR1 2 RPS6KB1 (220, 2 29) 6 FRAP1 RB1 RPS6KB1 3131) 2.38 2.25 37 1 1 orse,,(33.3%) :CCNDi (-2.52) AKT1 CCND1 CCND2 3 RPS6KB1 (2.20, 2 29) EVEROLIMUS CCND3 EIF4E EIF4EBP1 (Certican) EIF4G1 FRAP1 RB1 RPS6 (+2) CCND1 (-2.52) CCND2 438 225 20 RPS6KB1(27.3%) RPS6KB1 7.50, -3.63, -13.98) Cellular. Cyclin-dependant kinase inhibitors Cellular >> Cyclin-dependant kinase inhibitors> All BAG1 BAX BCL2 BCL2L1 13 CASP3 (2.14) CASP8 (8.85, 35 BID BIRC3 BIRC5 CASP3 (7+6) 2.67) CCNB1 (3.17) CDC2 3.91 3 04 60 (HMR-1275, Alvocidib) CASP8 CASP9 CCNB1 (37.1%) (2.92) CDK6 (2.18, 2.25) WO 2011/003911 PCT/EP2010/059648 207 CCND1CDC2 CDK2 CDK8 (2.39) PARP1 (2.68) CDK4 CDK5 CDK6 CDK7 CDK8 CDK9 CDKN1A BIRC3 (-2.84) CCND1 (-2.52) CDKN1B CYCS DIABLO CDKN1A (.7.85, -4.44) EGFR EGFR H2AFX HTRA2 (-12.64) MAPK8 (-2.06) MAPK14 MAPK8 MCL1 MCL1 (-3.34) PARP1 PYGM RB1 TP53 XIAP BAX BIRC5 CASP3 CASP3 (2.14) CDC2 (2.92) ROSCOVITINE CASP9 CDC2 CDK4 CDK5 5 CDK6 (2.18, 2.25) i17 CDK6 CDKN1A CYCS (3+2) 3.35 2.43 42 bDIABLO MCL1 PDCD8 (29.4%) CDKN1A (.7.85, -4.44) MCL1 RB1 SP1 TP53 XIAP (-3.34) Cellular >> Other Cellular >> Other >> All AFLIBERCEPT 1 VEGFA (1+0) VEGFA (2.60) 2.60 2 60 259 (VEGF Trap) (0.% (100.0%) DENILEUKIN 1 DIFTITOX 3 IL2RA IL2RB IL2RG (0+1) IL2RA (-2.77, -4.16) 3.47 0 00 0 (Ontak) (33.3%) Other/ Ungrouped Other / Ungrouped >> Other Other ! Ungrouped >> Other >> Other ABCB1 ABCG2 ABL1 ABL1 (3.58) AKAP12 (4.65, ACAA2 ADCY9 AFAP1 2.82) AKR1 B1 (18.32) AKAP12 AKR1B1 AKT1 ATP8A1 (3.45) ATR (2.33) ALAS1 ALDH6A1 ALDOC BACH2 (2.04) BAMBI (2.21) ALG13 ALOX5AP ANK3 BECN1 (2.18) BLVRB (2.08) ANKRD12 ANPEP ANXA2 BLZF1 (2.70) CAPN10 (2.13, AP3S1 AQP9 ARL61P1 7.15, 2.57) CASP3 (2.14) ARL6|P5 ARL7 ARMC9 CASP8 (8.85, 2.67) CCNB1 ASNS ASS1 ATP1B1 (3.17) CCNB2 (4.19) ATP2B1 ATP2C1 ATP5A1 CDC42BPA (2.48) CDKN2A ATP5 ATP8A1 ATR (18.18, 9.22) CDKN3 (5.50) AZGP1 BACH2 BAMBI CHEK1 (4.69) CHEK2 (2.55) BAX BCL2 BCL2A1 CHGB (3.36, 6.71) CLGN BCL2L1 BCMO1 BCR (3.78) CMTM8 (3.87) DNMT1 ARSENIC TRIOXIDE BECN1 BHLHB2 BID 246 (2.20) DNMT3B (4.55) (Arsenite Arsenolte, 49 BIRC5 BLVRB BLZF1 (91+154+1 DUSP4 (2.60) EPHX1 (4.56) 6 01 542 99 Arsodent, Claude//fte, 6 BNIP3 BNIP3L BPI ) ERC2 (7.66, 17.63) FGFR1 Claudetite Tisenox) BRDG1 BTBD2 BTBD3 (49.6%) (6.81, 6.75, 7.28, 9.06, 5.07, C140RF105 C160RF58 6.12) FOXRED2 (2.33) FRK C50RF13 C60RF48 (20.73) FTH1 (2.42, 2.32, C80RF4 CACNA2D2 2.35) GABPB2 (3.34, 5.13) CAPN10 CAPZB CARS GCLM (38.53) GSTA1 CASP10 CASP3 CASP8 (16.89) GSTP1 (3.00) HCG18 CASP9 CAV1 CAV2 (2.45, 2.85, 2.24) HCRTR1 CCDC102B CCDC52 (2.33) HIG2 (9.01) HIST3H3 CCL15 CCL2 CCL23 (2.04) HMGA1 (2.56, 6.05) CCNA1 CCNB1 CCNB2 HSPA1A (4.07,2.39) HSPB1 CCND1 CD1D CD44 CD52 (4.23, 3.70, 3.27) IFRD1 CD70 CD86 CDC42BPA (6.54, 6.75) IGFBP2 (2.42) CDC73 CDKN1A CDKN2A IKBKB (2.08, 2.59, 3.48) CDKN2B CDKN3 CEBPE IQCH (2.10) KCNH2 (4.05) CEP57 CFB CFLAR KLF5 (2.46) KYNU (2.22, WO 2011/003911 PCT/EP2010/059648 208 .~:HE1CHEK2 CHGB 2.53) LAT (2.44) MAFG. ........ CH13L1 CHST6 CIDEB (3.54, 5.15) MAP7 (4.36, CITED2 CKAP4 CLC 4.74) MCM2 (3.39) ME1 CLEC7A CLGN CMTM8 (8.79, 22.62, 9.07) NPAS1 CNOT2 COBLL1 (4.81) NUP107 (2.16) ORC4L COLEC10 COPS CREB5 (2.05) PAQR6 (2.43) PARP1 CRH CRIM1 CSF2RB (2.68) PAWR (2.19) PIR CSH1 CSPP1 CSRP3 (2.63) PTRH2 (2.31) RNF24 CST3 CTNNA1 CTSH (2.24) RPL23 (5.58) CX3CL1 CYB5R3 CYBA RPS6KB1 (2.20, 2.29) CYBRD1 CYCS CYLD RSL1D1 (2.55) SCPEP1 CYPlAl CYP39A1 (2.35) SLC2A1 (22.83) CYP3A43 CYTLI DAXX SLC44A1 (2.84) SLC5A12 DAZ4 DBC1 DDIT3 DEFA1 (6.44, 4.94) SMC4 (2.40) DEFA4 DKK1 DNAJB4 SOD1 (2.68, 2.37) SOX4 DNAJC9 DNMT1 DNMT3A (2.17) SYNJ2 (4.91, 9.38) DNMT3B DUSP4 TERT (3.67) TFAP2C (3.61) DYNC1H1 DYRK1B EB12 TNFRSF25 (7.01) TOP2A EGFR EGR1 ELA2A (5.88) TP53111 (2.08, 2.00) ENDOGL1 EP300 TRIP13 (8.20) TRMT1 (2.04) EPB41L2 EPHX1 EPX . TYMS (3.69) UBE2C (9.47) ERC2 ERGIC2 ESR1 UCHL1 (26.51) VEGFA ESR2 EVL F3 FAM110B (2.60) WDR12 (4.78) ZMYM2 FAM46C FANCC FAS (3.03, 4.70) ZNF334 (12.87) FASLG FBLN5 FBXL7 ZNF682 (3.69, 7.03) ZWINT FGFR1 FGFR2 FGR :(6.72) FNBP1L FOS FOXRED2 FRK FSCN1 FTH1 FYB ABCB1 (-3.59) ACAA2 ( GABBR2 GABPB2 2.69) ADCY9 (-2.46) GADD45A GBA GCLM ALDH6A1 (-2.90) ALDOC ( GIMAP6 GIT2 GJA1 GLA .4.73) ALOX5AP (.5.07, -7.13) GNAL GPM6A GPR30 ANK3 (-9.06, -7.92, .7.43) GPR44 GPRC5D GPX1 ANPEP (-5.41) AQP9 ( GRAMD3 GRK4 GSTA1 13.82) ARL61P5 (-3.81, -3.50) GSTP1 GSTZ1 H2AFX ARMC9 (-5.30, -5.49) ASNS HCG18 HCRTR1 HGF (-3.65) ASS1 (-2.76) BCL2A1 HIFlA HIG2 HIST1H2BM (-18.59, -2.78) BCR (-2.40) HIST1H4C HIST3H3 HK1 BHLHB2 (-2.28) BTBD3 ( HLA-B HLA-C HLA-DRA 2.40, -2.31) CACNA2D2 ( HLA-F HLA-G HMGA1 17.47) CAV1 (-22.26, -24.68) HMGB2 HMGN2 HMOX1 CAV2 (-14.35, -7.56) HSD17B2 HSF1 HSP27 CCDC102B (-2.74, -2.50) HSPA1A HSPA4 HSPA5 CCL15 (-4.50) CCL2 (-8.86) HSPA6 HSPB1 HTATIP2 CCL23 (-15.78, -18.04) ICAM3 D ID2 IDS IER2 CCND1 (-2.52) CD44 (-5.46) :F116 IFIH1 IFNA2 IFNG CD52 (-5.80) CD86 (-5.13, IFRD1 IGFBP2 IGFBP7 2.01) CDKN1A (-7.85, -4.44) IKBKB IL12RB2 IL24 IL6 CFB (-9.87) CH13L1 (-3.97, IL8RB INPP5B INSM1 12.11) CHST6 (-15.14) IQCH IRF1 ITGA2B CITED2 (-2.04) CKAP4 ( ITGAM ITGB1 ITGB3BP 3.35) CLEC7A (-2.69, -3.19) ITGB7 ITM2A JUN JUND COBLL1 (-3.44) CREB5 ( KCNH2 KCTD12 9.32, -11.86) CSF2RB (-2.47) KIAA0087 KIAA1609 CST3 (-2.29) CTSH (-3.47) KIF21B KITLG KLF11 CX3CL1 (-3.73, -9.86, -4.60) KLF5 KRT6A KYNU CYB5R3 (-2.89, -2.40, -2.01) LAMC1 LAT LGALS9 CYBRD1 (-2.20) CYTLI ( LGMN LHFP LHX6 LSP1 6.17) DBC1 (-3.01) DKK1 ( LTC4S MAFF MAFG 16.44) EB12 (-5.77) EGFR ( MAFK MAN 1lAl MAOA 12.64) EGRi (-604) ELA2A WO 2011/003911 PCT/EP2010/059648 209 .~~~~:AP2K3 MAP2K4........(.13) EVL(-2.26. 3(-3.63)....... MAP2K6 MAP7 MAPK1 FAM1 10B (-3.73) FAM46C ( MAPK14 MAPK3 MAPK7 2.08) FAS (-5.93) FBLN5 ( MAPK8 MAPK9 MARCH2 2.08) FBXL7 (-4.06) FGFR2 MASK MCF2L MCM2 ME1 (-31.03, -2.09, -16.52) FGR ( MK1671P MMP1 MMP2 6.72, -9.45, -8.54) FYB ( MMP28 MMP9 MPL MPO 2.23) GIMAP6 (-2.66) GJA1 MRPS16 MSC MSRB2 (-9.56) GPM6A (.34.94) MT1E MT1G MT1H MT1L GRAMD3 (-2.12) HGF (-2.73, MT1X MT2A MTA1 -6.54) HLA-DRA (-4.55) MXRA7 MYBL1 MYC HSD17B2 (-17.74) HSPA5 ( MYCN MYO1B MYST4 2.00) HSPA6 (-4.66) D ( N4BP1 N4BP2L1 NAB2 2.07) ID2 (-2.96, -3.38) IF116 NAV3 NCF1 NCF2 ND4 (-3.75, -3.71) ILS (-39.79) NEU3 NFE2L1 NFKBIA IL8RB (-3.46) IRF1 (-2.23) NFKBIE NME1 NPAS1 ITGAM (-3.25) ITGB3BP ( NPTX2 NR3C1 NUCB2 2.46) ITM2A (-3.63) JUN ( NUDT18 NUP107 OBSL1 2.62) KCTD12 (-4.62) KITLG ORC4L OS9 P4HA1 (-2.48, -3.23, -6.48) KRT6A ( P53AIP1 PAFAH2 PAOX 11.07) LGALS9 (-2.05) PAQR6 PARP1 PAWR LGMN (-2.41) LHFP (-3.42) PCNA PCSK5 PDCD4 LSP1 (-2.10) LTC4S (-2.70) PDE4A PDE4B PDE4DIP MAFF (-3.57) MANlAl ( PDLIM7 PDPK1 PDZD2 2.01) MAOA (-3.11) MAP2K3 PECAM1 PEX3 PGC PGF (-3.18, -3.56) MAP2K6 (-4.53) PHLDA2 PIGO PIK4CA .MAPK8 (-2.06) MARCH ( PIR PKNOX1 PLAUR 3.34, -3.08) MMP1 (-58.24) PLEKHO1 PLXNC1 PML MMP2 (-4.48) MMP28 (-8.08, PPGB PPIH PPP1CB -14.29) MMP9 (-10.90) MT1E PRG3 PRKCB PRR16 (-15.67) MT1G (-11.42, -6.38) PRSS1 PSMB6 PSMB8 MT1H (-8.15, -8.00) MT1L ( PTGFR PTRH2 PTTG1 11.08) MT1X (-8.45, -9.49) PURG PXDN RAB27B MT2A (-8.59, -9.15) MXRA7 RACGAP1 RAPGEF4 (-2.88, -4.06, -3.93, -3.20) RARA RASSF8 RBMX MYO1B (-3.04, -2.30, -2.61) RELA RERE RFC2 RGS2 NAV3 (-6.48, -8.99) NCF1 ( RNASEH2A RNF144A 3.16, -3.30) NCF2 (-3.56) RNF24 RNF6 ROBO1 NFKBIA (-4.00) NPTX2 ( RPL17 RPL18A RPL23 11.45) NUCB2 (-3.02, -3.50) RPL7 RPS15A RPS6KB1 NUDT18 (-4.22) OBSL1 ( RRBP1 RSL1D1 RYK 5.40, -4.33) P4HA1 (-2.49) S10A1O0 S100A8 S100A9 PDE4B (-5.96) PDE4DIP ( SATB2 SCAMP5 6.65, -2.11, -2.32, -2.90) SCGB2A2 SCPEP1 SCT PDLIM7 (-2.16) PDZD2 ( SDC2 SDC4 SELP 33.94) PECAM1 (-4.62) PGC SERPINB1 SERTAD2 (-3.76, -25.02) PGF (-2.56) SFRS5 SH3GL2 SIGLEC6 PHLDA2 (-3.07) PLAUR ( SLC14A2 SLC15Al 5.48) PLEKHO1 (-2.69) PML SLC1A2 SLC22A18 (-2.88, -3.32, -3.67, -3.53) SLC2A1 SLC44A1 PPP1CB (-5.15, -2.92) SLC5A12 SLC6A5 PRR16 (-16.52) PTGFR ( SLCO3A1 SMAD1 SMC4 7.80) PXDN (-3.39, -3.20) SOD1 SOX18 SOX30 RASSF8 (-5.90, -17.88, SOX4 SP1 SPTAN1 2.19, -10.81) RGS2 (-9.73) ST6GALNAC4 STAB1 RNF144A (-2.56) RPL17 ( STAT1 STATH SULF1 3.08, -2.59, -2.80) RPL7 ( SUSD5 SYN3 SYNJ2 2.15, -2.41) S100A8 (-10.28) TCF4 TERC TERF1 S100A9 (-2.26) SDC2 (-3.99) TERF2 TERT TFAP2C SELP (-4.50) SERPINB1 ( TFi TGFBR2 TIMPi 2.41) SH3GL2 (-5.52) WO 2011/003911 PCT/EP2010/059648 210 TIMP2 TMEM158 TNF SMAD1 (-3.04) SUSD5( TNFAIP3 TNFAIP8 2.47) TCF4 (-3.68, -2.69) TNFRSF1B TNFRSF25 TGFBR2 (-3.67, -4.16) TIMP1 TNFRSF9 TNFSF13 (-4.76) TIMP2 (-3.34) TNFSF8 TOP2A TP53 TNFAIP3 (-3.78, -3.05) TP53111 TP73 TPT1 TNFAIP8 (-3.52) TNFSF13 ( TRA2ATRAF3IP2 TRIM16 4.94) TPT1 (-2.57, -2.61, TRIP13 TRMT1 TSC22D3 2.20) UBE2D1 (-2.48) VNN1 TTC38 TXNIP TYMS (-2.97, -2.90) WT1 (-2.49) UBE2C UBE2D1 UCHL1 UCP2 VEGFAVNN1 DNMT3A (2.41, -2.89) WDR12 WDR82 WSB2 WT1 XIAP XRCC6 ZFP36 ZFP36L1 ZMYM2 ZNF334 ZNF37A ZNF682 ZNF771 ZWINT AKT APAF1 BAK1 BAX BCL2 BCL2L1 BCL2L11 BID BIRC2 BIRC3 BIRC5 CA9 CSP3 ASP7BCL2L1 1 (3.13) CASP3 CA9 CASP3 CASP7 CASP8 CASP9 CCND1 (2.14) CASP8 (8.85, 2.67) CDKN1A CDKN1B CFLAR CYP2C9 (3.97) CYP2D6 CYCS CYP1A2 CYP2C19 . (3.52, 3.15) NFKBIB (2.90, CYP2C9 CYP2D6 CYP3A4 2.95, 2.86) PARP1 (2.68) PMAIP1 (12.40) PSMVB5 ZDDIT3 DFFA DIABLO 18 BOREZOMIB 61 HIFlA HSPA5 HTRA2 JUN (9+9) (2.22) 3.99 4.28 63 eMAP2K1 MAP2K4 MAPK1 (29.5%) MAPK4 MAK3 MPK8BIRC3 (-2.84) CCND1 (-2.52) MAPKI4 MAPK3 MAPK8 MCL1 NFKB1 NFKBIA CKN1A (.7.85, -4.44) NFKBIB NFKBIE PARP1 CYP3A4 (-7.84) HSPA5 ( PDCD8 PMAIP1 PSMB1 2.00) JUN (-2.62) MAPK8 ( PSMB2 PSMB5 PSMD1 2.06) MCL1 (-3.34) NFKBIA PSMD2 RAF1 RB1 RELA (-4.00 SFN STAT3 TNF TP53 TRAF2 XIAP AKT1 BIR CASP3 CASP3 (2.14) CYP19A1 (2.64) NRG1 (2.37) PARP1 CELCOIBCCND1 CYP19AI ILIB 11-6: 8 (17 MAP2K1 MAP2K2 NRG1 (4+4) CCN 112.38 2.46 57 (Celebra, Celebrex) PRiPR TG2 (71 CCNDi (-2.52) ILi B (-12.47) PARP1 PDPK1 PTGS2 (47.1%) RELA STS SULT2A1 TNF 1L6 (-39.79) PTGS2 (-36.04, 32.79) ABCB1 ABCC1 CD59 ABCC1 (7.70) IKBKB (2.08, COLCHICINE CUGBP2 CYP3A4 IKBKB 2.59, 3.48) JUN MAPK8 MAPK9 9 ABCB1 (-3.59) CD59 (-8.71, Colb e21 MEFV NFATC4 NFKB1 (2+7) 4.89, -4.77, -7.00) CUGBP2 4.96 5 21 49 C ndyon, NFKBIA NR3C1 PTK2B (42.9%) (-7.53, -8.05) CYP3A4 (.7.84) nRALBP1 RELA TAT TP53 JUN (-2.62) MAPK8 (-2.06) TUBB1 TUBB2A NFKBIA (-4. 00) OBLIMERSEN (Genasense, 2 BCL2 IGH-6 Augmerosen) TEGA.UR . 1 CYP2A6 (UFT) TIPIFARNIB ABCB1 CYP3A4 CYP3A5 ABCB1 (-359) CYP3A4( (Zrnsta UT11(0+3) -. 9CY34 15.46 0.00 0 (Zaestra) UGTA750%) 7.84) CYP3A5 (-34.94) VORINOSTAT 47 AKT1 BAK1 BAX BCL2 17 BCL2L11 (313) CASP2 4.07 3 36 71 WO 2011/003911 PCT/EP2010/059648 211 (Zolinza) . BCL2A. BCL2L1 BCL2L1 1 (10+7) (2.27) CASP3 (2.14) CASP8 BID BIRC2 BIRC3 BIRC5 (36.2%) (8.85, 2.67) HDAC1 (6.44) CASP2 CASP3 CASP7 HDAC2 (4.18) HDAC6 (2.00) CASP8 CASP9 CCND1 HIST3H3 (2.04) PARP1 CDKN1A CDKN1B CFLAR :(2.68) RARB (2.96) CYCS CYP1Al CYP1B1 DIABLO ERBB2 HDAC1 BCL2A1 (-18.59, -2.78) HDAC2 HDAC3 HDAC6 : BIRC3 (-2.84) CCND1 (-2.52) HDAC8 HIST3H3 HTRA2 CDKN1A (.7.85, -4.44) MAP2K1 MAPK1 MAPK14 CYP1B1 (-3.87) MAPK8 ( MAPK3 MAPK8 NFKB1 2.06) TNFSF10 (-7.51) PARP1 PDCD8 RAF1 RARB RB1 RELAY TNF TNFSF1O XIAP H:mtUnIenvovcn\NRPorthhDCC\FMn736340_)dowx-24/2/2014 211a Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Claims (15)

1. A method for predicting the relative efficacy of a plurality of drugs for treating a cancer in a patient comprising: - characterizing molecular anomalies of a tumour sample from the patient in comparison to a normal sample from the same patient, thereby determining the deregulated genes in the tumour; - providing a database comprising the target genes for each drug of the plurality of drugs; - determining a score for each drug of the plurality of drugs essentially based on the percentage of deregulated genes among the target genes for each drug in the tumour sample from the patient, thereby a higher score is predictive of a higher relative efficacy of the drug for treating the tumour in the patient.

2. The method according to claim 1, wherein the step of characterizing molecular anomalies of a tumour sample comprises determining the genes differentially expressed in the tumour in comparison to the normal sample, and/or determining the gain or loss of gene copy number and/or detecting the presence of a mutation in a gene.

3. The method according to claim 2, wherein the step of characterizing molecular anomalies of a tumour sample comprises determining a fold change (F) for the differentially expressed genes and/or for the gain or loss of gene copy number and, optionally, further determining the intensity of the gene transcription (Int) for the differentially expressed genes.

4. The method according to any one of claims 1-3, wherein the target genes for each drug are classified in the database into the major target genes (MC), the minor target genes (Mc) and the resistance genes (CR).

5. The method according to any one of claims 1-4, wherein the score (W) for a given drug is determined by the following algorithm: H:nitlnterwovcn\NRPostbhDCC\FMfl7326340_ doo-24112/204 213 ( c>2) W = P z ncFW2 wherein W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the tumour of the patient; z is an optional multiplication coefficient associated to the presence of a mutation in a target gene of the given drug; E is sum; Fc>2 is the fold change of each deregulated target gene for the given drug with a Fold Change higher than 2; nCFc>2 refers to the number of target genes for the given drug with a Fold Change higher than 2.

6. The method according to claim 5, wherein Fc>2 is the Fold Change of each over-expressed target gene for the given drug with a Fold Change higher than 2 and nCFc>2 is either the number of target genes for the given drug with a Fold Change higher than 2, or the number of over-expressed target genes for the given drug with a Fold Change higher than 2.

7. The method according to any one of claims 1-4, wherein the score (W) for a given drug is determined by the following algorithm: ( ZCM FCs ) (em Fcm ) LCR FCR W =P qz,+q2 z2 - q3 Z3 nwCM n 2 Cm n 3 CR wherein W is the score for the given drug; P is the percentage of target genes for the given drug which are deregulated in the tumour of the patient; E is sum; CM refers to major target genes for the given drug; Cm refers to minor target genes for the given drug; H fmIn[tencvc\NRPothW\CCfl\ FM7326340_l dccx-24{I2/2O14 214 CR refers to resistance genes for the given drug; niCM, n 2 Cm and nsCR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes; FCM, Fem and FCR are the Fold change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively; qi, q2 and q3 are optional multiplication coefficients for major target genes, minor target genes and resistance genes, respectively; Zi, Z 2 and z3 are optional multiplication coefficients associated to the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively.

8. The method according to any one of claims 1-4, wherein the score (W) for a given drug is determined by the following algorithm: PoM( kcM FcM) Ecm( Lcm FCM) PCR( cR FCR) W = Iqq z1 + q 2 Z2 - q 3 Z3 nCM n 2 Cm n 3 CR wherein the meaning of W, E, CM, Cm, CR, FCM, Fem, FCR, qi, q2, q3, Zi, Z2 and z 3 are as defined above and PCM, Pcm and PCR are the percentage of genes for the given drug which are deregulated in the tumour of the individual for major target genes, minor target genes and resistance genes, respectively

9. The method according to any one of claims 1-4, wherein the score (W) for a given drug is determined by one of the following algorithms: ( cM FcM x ItMM) ( Ycm Fcm x Intcm) ( ECR FCR X IneR) W= P q, z1 + q2 Z2 -q3 Z n,CM n 2 Cm n 3 CR or PCM( 5 OM FcM x IntcM) Ecm(Ycm Fcm x Int.m) eCR( 5 cR FCR X IntcR) VV=I qz1+ q2Z2- q 3 z 3 n 1 CM n 2 Cm n 3 CR wherein the meaning of W, E, CM, Cm, CR, FCM, Fer, FCR, qi, q2, q3, Zi, Z2 and Z3, and if present PcM, Pcm and PCR, are as defined above and IntcM, Intcm and IntcR are the intensity for major target genes, minor target genes and resistance genes, respectively. H:\m1\1nenvove\NR orbl\DCC\M% 7326340O1dgx-24/t2/204 215

10. The method according to any one of claims 1-4, wherein the score (W) for a given drug is determined by one of the following algorithms: ( ECM FCM )C( OR FCR ) W =P q1 z1, q 3 n 1 CM n 3 CR PCM( CM FCM) PCR ( FOtCR W =q1 z1 - q. z. n1 CM nCCR or ( ESCM FCM X Itm) MCR FCRx X nCR) W =P n~m-qq z1 - n Rq3 Zs3 or PCM (ECM FCMx I MCM) PCR (CR FCR X IntCR) W =q1 z1 - q3 Z3 n 1 CM n 3 CR wherein the meaning of W, Z, CM, CR, FcM, FcR, qi, q3, zi and z 3 , and if present PcM, PCR, InteM, and IntcR are as defined above.

11. The method according to any one of claims 7-10, wherein FcM, Fcm and FCR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and niCM, n 2 Cm and nsCR are either the number of target genes for the given drug with the defined threshold, or the number of over-expressed target genes for the given drug with the defined threshold.

12. The method according to claim 11, wherein the defined threshold is a Fold change of at least 2 or higher than 2.

13. The method according to any one of claims 7-12, wherein multiplication coefficients for the target genes can be comprised between 10 and 1,000 for major target genes H:fmlntenvovcp\RPcrtbIDCC\FMl\7326340 Idox-24/12/D214 216 (qi), 0.1 and 10 for minor target genes (q2) and 10 to 1,000 for resistance genes (q3).

14. The method according to any one of claims 7-13, wherein multiplication coefficients associated to a mutation Z 1 , z2 and Z3 are 1 when no mutation exists and, depending on the functional impact of the mutation, can be comprised between 10 and 1,000.

15. The method according to any one of claims 1-14, substantially as hereinbefore described with reference to the Examples and/or Figures.